Integrating large language models (LLMs) like GPT-4 into medical ethics is a novel concept, and understanding the effectiveness of these models in aiding ethicists with decision-making can have significant implications for the healthcare sector. Thus, the objective of this study was to evaluate the performance of GPT-4 in responding to complex medical ethical vignettes and to gauge its utility and limitations for aiding medical ethicists. Using a mixed-methods, cross-sectional survey approach, a panel of six ethicists assessed LLM-generated responses to (...) eight ethical vignettes.The main outcomes measured were relevance, reasoning, depth, technical and non-technical clarity, as well as acceptability of GPT-4’s responses. The readability of the responses was also assessed. Of the six metrics evaluating the effectiveness of GPT-4’s responses, the overall mean score was 4.1/5. GPT-4 was rated highest in providing technical (4.7/5) and non-technical clarity (4.4/5), whereas the lowest rated metrics were depth (3.8/5) and acceptability (3.8/5). There was poor-to-moderate inter-rater reliability characterised by an intraclass coefficient of 0.54 (95% CI: 0.30 to 0.71). Based on panellist feedback, GPT-4 was able to identify and articulate key ethical issues but struggled to appreciate the nuanced aspects of ethical dilemmas and misapplied certain moral principles.This study reveals limitations in the ability of GPT-4 to appreciate the depth and nuanced acceptability of real-world ethical dilemmas, particularly those that require a thorough understanding of relational complexities and context-specific values. Ongoing evaluation of LLM capabilities within medical ethics remains paramount, and further refinement is needed before it can be used effectively in clinical settings. (shrink)

In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificially intelligent systems, such as large language models, replacing human subjects in empirical moral psychology. We argue that human subjects are in various ways indispensable.

In some languages every statement must contain a specification of the type of evidence on which it is based: for example, whether the speaker saw it, or heard it, or inferred it from indirect evidence, or learnt it from someone else. This grammatical reference to information source is called 'evidentiality', and is one of the least described grammatical categories. Evidentiality systems differ in how complex they are: some distinguish just two terms (eyewitness and noneyewitness, or reported and everything else), while (...) others have six or even more terms. Evidentiality is a category in its own right, and not a subcategory of epistemic or some other modality, nor of tense-aspect. Every language has some way of referring to the source of information, but not every language has grammatical evidentiality. In English expressions such as I guess, they say, I hear that, the alleged are not obligatory and do not constitute a grammatical system. Similar expressions in other languages may provide historical sources for evidentials. True evidentials, by contrast, form a grammatical system. In the North Arawak language Tariana an expression such as "the dog bit the man" must be augmented by a grammatical suffix indicating whether the event was seen, or heard, or assumed, or reported. This book provides the first exhaustive cross-linguistic typological study of how languages deal with the marking of information source. Examples are drawn from over 500 languages from all over the world, several of them based on the author's original fieldwork. Professor Aikhenvald also considers the role evidentiality plays in human cognition, and the ways in which evidentiality influences human perception of the world.. This is an important book on an intriguing subject. It will interest anthropologists, cognitive psychologists and philosophers, as well as linguists. (shrink)

Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other things, Habermas's inquiries (...) into the analogy between human minds and computers, and into the status of atypical participants in the linguistic community such as genetically modified subjects and animals. Major conclusions are the LLMs seem to qualify as authors that originally participate in discursive practices but do display only a structurally derivative form of communicative competence and fail to meet the status of communicative agents. In this sense, while the contribution of AI writing systems in public discourse and deliberation can support the process of mutual understanding within the community of speakers, the human actors involved in the development, use, and diffusion of these systems share a collective responsibility for the disclosure of AI authorship and verification and adjudication of validity claims. (shrink)

Cohen (2023) takes a fair and measured approach to the question of what ChatGPT means for bioethics. The hype cycles around AI often obscure the fact that ethicists have developed robust frameworks...

The first volume to offer a thorough and systematic account of evidentiality and the expression of information source, Illustrated with extensive data from a range of typologically diverse languages, Introductory chapter offers practical advice for fieldworkers investigating evidentially, Interdisciplinary in nature with insights from typology, semantics, pragmatics, language description, anthropology, cognitive psychology, and psycholinguistics Book jacket.

Knowledge can be expressed in language using a plethora of grammatical means. Four major groups of meanings related to knowledge are Evidentiality: grammatical expression of information source; Egophoricity: grammatical expression of access to knowledge; Mirativity: grammatical expression of expectation of knowledge; and Epistemic modality: grammatical expression of attitude to knowledge. The four groups of categories interact. Some develop overtones of the others. Evidentials stand apart from other means in many ways, including their correlations with speech genres and social environment. (...) This essay presents a framework which connects the expression of knowledge across the world's languages in a coherent way, showing their dependencies and complexities, and pathways of historical development in various scenarios, including language obsolescence. (shrink)

Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires (...) and beliefs, and then make and update plans to pursue their desires given their beliefs. We argue that the rise of language agents significantly reduces the probability of an existential catastrophe due to loss of control over an AGI. This is because the probability of such an existential catastrophe is proportional to the difficulty of aligning AGI systems, and language agents significantly reduce that difficulty. In particular, language agents help to resolve three important issues related to aligning AIs: reward misspecification, goal misgeneralization, and uninterpretability. (shrink)

In this article, drawing upon a feminist epistemology, we examine the critical roles that philosophical standpoint, historical usage, gender, and language play in a knowledge arena which is increasingly opaque to the general public. Focussing on the language dimension in particular, in its historical and social dimensions, we explicate how some keywords in use across artificial intelligence (AI) discourses inform and misinform non-expert understandings of this area. The insights gained could help to imagine how AI technologies could be (...) better conceptualised, explained, and governed, so that they are leveraged for social good. (shrink)

A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists (...) in syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

The article argues that cognitive linguistic theory may prove an alternative to the Montague paradigm for designing natural language understanding systems. Within this framework it describes a system which models language understanding as a dialogical process between user and computer. The system operates with natural language texts as input and represent language meaning as entity-relationship diagrams.

Current work in language models (LMs) helps us speed up or even skip thinking by accelerating and automating cognitive work. But can LMs help us with critical thinking -- thinking in deeper, more reflective ways which challenge assumptions, clarify ideas, and engineer new concepts? We treat philosophy as a case study in critical thinking, and interview 21 professional philosophers about how they engage in critical thinking and on their experiences with LMs. We find that philosophers do not find LMs (...) to be useful because they lack a sense of selfhood (memory, beliefs, consistency) and initiative (curiosity, proactivity). We propose the selfhood-initiative model for critical thinking tools to characterize this gap. Using the model, we formulate three roles LMs could play as critical thinking tools: the Interlocutor, the Monitor, and the Respondent. We hope that our work inspires LM researchers to further develop LMs as critical thinking tools and philosophers and other 'critical thinkers' to imagine intellectually substantive uses of LMs. (shrink)

This paper explores the intersection of AI hallucinations and the question of AI consciousness, examining whether the erroneous outputs generated by large language models (LLMs) could be mistaken for signs of emergent intelligence. AI hallucinations, which are false or unverifiable statements produced by LLMs, raise significant philosophical and ethical concerns. While these hallucinations may appear as data anomalies, they challenge our ability to discern whether LLMs are merely sophisticated simulators of intelligence or could develop genuine cognitive processes. By analyzing (...) the causes of AI hallucinations, their impact on the perception of AI cognition, and the potential implications for AI consciousness, this paper contributes to the ongoing discourse on the nature of artificial intelligence and its future evolution. (shrink)

Language, one of humanity’s major transformative innovations, is foundational for many cultural, artistic, scientific, and economic advancements, including the creation of artificial intelligence (AI). However, in the fight against climate change, the power of such innovation is constrained due to the boring language of climate science and science communication. In this essay, we encapsulated the situation and risks of boring language in communicating climate information to the public and countering climate denialism and disinformation. Based on the Serendipity-Mindsponge-3D (...) knowledge management framework, we recommend several strategies for climate scientists and science communicators to be more creative and make their communication more interesting, including collaboration with other cultural sectors (e.g., stand-up comedians, climate fiction, etc.) and AI. (shrink)

As AI technologies are rolled out into healthcare, academia, human resources, law, and a multitude of other domains, they become de-facto arbiters of truth. But truth is highly contested, with many different definitions and approaches. This article discusses the struggle for truth in AI systems and the general responses to date. It then investigates the production of truth in InstructGPT, a large language model, highlighting how data harvesting, model architectures, and social feedback mechanisms weave together disparate understandings of veracity. (...) It conceptualizes this performance as an operationalization of truth, where distinct, often-conflicting claims are smoothly synthesized and confidently presented into truth-statements. We argue that these same logics and inconsistencies play out in Instruct’s successor, ChatGPT, reiterating truth as a non-trivial problem. We suggest that enriching sociality and thickening “reality” are two promising vectors for enhancing the truth-evaluating capacities of future language models. We conclude, however, by stepping back to consider AI truth-telling as a social practice: what kind of “truth” do we as listeners desire? (shrink)

This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves — rather than with their mathematical representations (as in symbolic (...) models). This paper uses the natural-language ABMA to test the robustness of symbolic reason-balancing models of argumentation (Mäs & Flache 2013; Singer et al. 2019): First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from symbolic models. However, once ADAs start to actively generate new contributions, the evolution of a conversation is dominated by properties of the agents as authors. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research. (shrink)

COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers (...) obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803. (shrink)

In recent years, AI research has become more and more computationally demanding. In natural language processing, this tendency is reflected in the emergence of large language models like GPT-3. These powerful neural network-based models can be used for a range of NLP tasks and their language generation capacities have become so sophisticated that it can be very difficult to distinguish their outputs from human language. LLMs have raised concerns over their demonstrable biases, heavy environmental footprints, and (...) future social ramifications. In December 2020, critical research on LLMs led Google to fire Timnit Gebru, co-lead of the company’s AI Ethics team, which sparked a major public controversy around LLMs and the growing corporate influence over AI research. This article explores the role LLMs play in the political economy of AI as infrastructural components for AI research and development. Retracing the technical developments that have led to the emergence of LLMs, we point out how they are intertwined with the business model of big tech companies and further shift power relations in their favour. This becomes visible through the Transformer, which is the underlying architecture of most LLMs today and started the race for ever bigger models when it was introduced by Google in 2017. Using the example of GPT-3, we shed light on recent corporate efforts to commodify LLMs through paid API access and exclusive licensing, raising questions around monopolization and dependency in a field that is increasingly divided by access to large-scale computing power. (shrink)

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of (...) language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored. (shrink)

A survey conducted in 2023 surveyed 3,017 high school and college students. It found that almost one-third of them confessed to using ChatGPT for assistance with their homework. The rise of Large Language Models (LLMs) such as ChatGPT and Gemini has led to a surge in academic misconduct. Students can now complete their assignments and exams just by asking an LLM for solutions to the given problem, without putting in the effort required for learning. And, what is more worrying, (...) educators do not have the proper tools to detect it. The more advanced AI tools become, the more human-like text they generate, and the more difficult they are to detect. Additionally, some educators find it difficult to adapt their teaching and assessment methods to avoid plagiarism. This paper is focused on how LLMs and AI-Generated Content (AIGC) have affected education. It first shows the relationship between LLMs and academic dishonesty. Then, it reviews state-of-the-art solutions for preventing academic plagiarism in detail, including a survey of the main datasets, algorithms, tools, and evasion strategies for plagiarism detection. Lastly, it identifies gaps in existing solutions and presents potential long-term solutions based on AI tools and educational approaches to address plagiarism in an ever-changing world. (shrink)

Member States of the United Nations proposed Seventeen Sustainable Development Goals (SDGs) in 2015, emphasizing the well-being of people, planet, prosperity, peace, and partnership. Countries are expected to work diligently to achieve these goals by the year 2030. The paths chosen to achieve the SDGs depend on each country’s specific needs, challenges, and opportunities. This contribution conducts a bibliometric study of selected SDG research related to hunger and climate change among countries of the North Asia Pacific region. A review of (...) literature related to the seventeen SDGs is provided to update the current research in SDGs as a background for the regional bibliometric search. The academic published literature related to this work were collected and text mined to create a research database for trend analysis. In this research, the natural language model KeyBERT and a text mining algorithm for normalized term frequency were used to extract key terminologies for bibliometric analysis. The study provides an overview of the seventeen SDGs in the literature review and then focuses on SDG 2 (zero hunger) and SDG 13 (climate action) publication trends. These two SDGs are in close alignment with sustainable food systems and agricultural development. The conclusion indicates that greater cooperation (SDG 17) is critical to improving the quality of life within the region and in building an ethical framework that restricts external exploitation of resources that benefit newly developed economies over developing economies. (shrink)

The potential for Large Language Models (LLMs) to attain technological singularity—the point at which artificial intelligence (AI) surpasses human intellect and autonomously improves itself—is a critical concern in AI research. This paper explores the feasibility of current LLMs achieving singularity by examining the philosophical and practical requirements for such a development. We begin with a historical overview of AI and intelligence amplification, tracing the evolution of LLMs from their origins to state-of-the-art models. We then proposes a theoretical framework to (...) assess whether existing LLM technologies could satisfy the conditions for singularity, with a focus on Recursive Self-Improvement (RSI) and autonomous code generation. We integrate key component technologies, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), into our analysis, illustrating how these could enable LLMs to independently enhance their reasoning and problem-solving capabilities. By mapping out a potential singularity model lifecycle and examining the dynamics of exponential growth models, we elucidate the conditions under which LLMs might self-replicate and rapidly escalate their intelligence. We conclude with a discussion of the ethical and safety implications of such developments, underscoring the need for responsible and controlled advancement in AI research to mitigate existential risks. Our work aims to contribute to the ongoing dialogue on the future of AI and the critical importance of proactive measures to ensure its beneficial development. (shrink)

Despite the exceptional performance of large language models (LLMs) on a wide range of tasks involving natural language processing and reasoning, there has been sharp disagreement as to whether their abilities extend to more creative human abilities. A core example is the interpretation of novel metaphors. Here we assessed the ability of GPT-4, a state-of-the-art large language model, to provide natural-language interpretations of a recent AI benchmark (Fig-QA dataset), novel literary metaphors drawn from Serbian poetry and (...) translated into English, and entire novel English poems. GPT-4 outperformed previous AI models on the Fig-QA dataset. For metaphors drawn from Serbian poetry, human judges – blind to the fact that an AI model was involved – rated metaphor interpretations generated by GPT-4 as superior to those provided by a group of college students. In interpreting reversed metaphors, GPT-4, as well as humans, exhibited signs of sensitivity to the Gricean cooperative principle. In addition, for several novel English poems GPT-4 produced interpretations that were rated as excellent or good by a human literary critic. These results indicate that LLMs such as GPT-4 have acquired an emergent ability to interpret literary metaphors, including those embedded in novel poems. (shrink)

Despite the exceptional performance of large language models (LLMs) on a wide range of tasks involving natural language processing and reasoning, there has been sharp disagreement as to whether their abilities extend to more creative human abilities. A core example is the interpretation of novel metaphors. Here we assessed the ability of GPT-4, a state-of-the-art large language model, to provide natural-language interpretations of a recent AI benchmark (Fig-QA dataset), novel literary metaphors drawn from Serbian poetry and (...) translated into English, and entire novel English poems. GPT-4 outperformed previous AI models on the Fig-QA dataset. For metaphors drawn from Serbian poetry, human judges – blind to the fact that an AI model was involved – rated metaphor interpretations generated by GPT-4 as superior to those provided by a group of college students. In interpreting reversed metaphors, GPT-4, as well as humans, exhibited signs of sensitivity to the Gricean cooperative principle. In addition, for several novel English poems GPT-4 produced interpretations that were rated as excellent or good by a human literary critic. These results indicate that LLMs such as GPT-4 have acquired an emergent ability to interpret literary metaphors, including those embedded in novel poems. (shrink)

If Large Language Models can make real scientific contributions, then they can genuinely use language, be systematically wrong, and be held responsible for their errors. AI models which can make scientific contributions thereby meet the criteria for scientific authorship.

The foundations of AI design discourse are worth analyzing. Here, attention is paid to the nature of theory languages used in designing new AI technologies because the limits of these languages can clarify some fundamental questions in the development of AI. We discuss three types of theory language used in designing AI products: formal, computational, and natural. Formal languages, such as mathematics, logic, and programming languages, have fixed meanings and no actual-world semantics. They are context- and practically content-free. Computational (...) languages use terms referring to the actual world, i.e., to entities, events, and thoughts. Thus, computational languages have actual-world references and semantics. They are thus no longer context- or content-free. However, computational languages always have fixed meanings and, for this reason, limited domains of reference. Finally, unlike formal and computational languages, natural languages are creative, dynamic, and productive. Consequently, they can refer to an unlimited number of objects and their attributes in an unlimited number of domains. The differences between the three theory languages enable us to reflect on the traditional problems of strong and weak AI. (shrink)

If AI is to emulate the language, mind, and work of humans, what remains of being human? One scenario is that humans are at risk of becoming robots of AI-powered systems, serving the interests of a few global corporations. We have already reached this stage of transformation. Given this predicament, the issues concerning the capacity of AI beyond the human should be addressed through a critique of AI ideology. Methodically, this would imply a shift in perspective, from the subject (...) of AI to the function and deceptive power of its intelligent devices. Do they serve us or do we serve them? Here, it is important not to follow the standard approach to AI as the prospect of creating super- human intelligence, to avoid the trap of basing the critique on the discourse of AI ideology. The goal, rather, is to revive the strength of philosophical critique and reestablish a certain idealism. (shrink)

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may (...) lead to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology. (shrink)

Roger Schank's research in AI takes seriously the ideas that understanding natural language involves mapping its expressions into an internal representation scheme and that these internal representations have a syntax appropriate for computational operations. It therefore falls within the computational approach to the study of mind. This paper discusses certain aspects of Schank's approach in order to assess its potential adequacy as a (partial) model of cognition. This version of the Language of Thought hypothesis encounters some of the (...) same difficulties that arise for Fodor's account. (shrink)

Language agents are AI systems that combine large language models with other elements to facilitate interaction with an environment. They include LLM-based chatbots but can have a wide range of additional features to support learning, reasoning and decision-making. Goldstein and Kirk-Giannini. Citationm.s. [AI wellbeing] argue that some language agents have beliefs and desires, but it is not obvious that they are agents at all, since they select outputs by querying language models. This paper investigates agency and (...) desires in language agents. (shrink)

Large language models (LLMs) such as ChatGPT are opening up new areas of research and teaching potential across a variety of domains. The purpose of the present conceptual paper is to map this new terrain from the point of view of Critical Discourse Studies (CDS). We demonstrate that the usage of LLMs raises concerns that definitely fall within the remit of CDS; among them, power and inequality. After an initial explanation of LLMs, we focus on three key areas of (...) reflection. The first is a general stock-taking, where we look at CDS’ theoretical underpinnings and what they imply for working with AI-generated language data. The second issue is authorship, where we assess the traceability of linguistic metadata and the ethically sensitive situation with regard to ownership of texts. The third area is linguistic homogenisation, where we examine how LLM usage privileges the mainstream. Afterwards, we explore ways in which LLMs could be used in research, and we discuss the implications of exploring their use in the classroom through a CDS lens. We close the paper with some observations on likely future developments in AI and how CDS can contribute with its distinctive theoretical, methodological and critical apparatus. (shrink)

Keywords are essential to the searchability and therefore discoverability of museum and archival collections in the modern world. Without them, the collection management systems (CMS) and online collections these cultural organisations rely on to record, organise, and make their collections accessible, do not operate efficiently. However, generating these keywords manually is time consuming for these already resource strapped organisations. Artificial intelligence (AI), particularly generative AI and Large Language Models (LLMs), could hold the key to generating, even automating, this key (...) data and as such be considered a co-creative add-on. This study contributes to the literature by introducing the use of Meta’s open-source LLM, Llama, to generate keywords from curator/archivist written descriptions of museum and archival collection items. Our findings suggest that these technologies add significant value compared to current manual methods for keyword generation. In particular, we find that through using carefully crafted prompts, successful keyword augmentations could be established making museum and archival collections much more accessible to wider and more diverse audiences. However, the results also showed that generative AI has biases (e.g., hallucinations, over generalisations, outdated language), though the frequency of occurrence was not as high as general perception may insist. Hence, we also discuss mitigation strategies to address these and how cultural institutions can recognise the risks and errors while getting the most from the systems. Finally, we discuss options to achieve structured results which allow easier ingestion of data back into CMS. Ultimately, LLMs hold significant potential to enhance accessibility to museum and archival collections, yet they are not without imperfection as we extensively discuss. (shrink)

Credences are mental states corresponding to degrees of confidence in propositions. Attribution of credences to Large Language Models (LLMs) is commonplace in the empirical literature on LLM evaluation. Yet the theoretical basis for LLM credence attribution is unclear. We defend three claims. First, our semantic claim is that LLM credence attributions are (at least in general) correctly interpreted literally, as expressing truth-apt beliefs on the part of scientists that purport to describe facts about LLM credences. Second, our metaphysical claim (...) is that the existence of LLM credences is at least plausible, although current evidence is inconclusive. Third, our epistemic claim is that LLM credence attributions made in the empirical literature on LLM evaluation are subject to non-trivial sceptical concerns. It is a distinct possibility that even if LLMs have credences, LLM credence attributions are generally false because the experimental techniques used to assess LLM credences are not truth-tracking. Our analysis has implications for the assessment of LLMs for properties like factuality and trustworthiness, and the broader scientific debate over the use of human cognition as a blueprint for understanding LLM behaviour. (shrink)

Modern generative AI systems have shown the capacity to produce remarkably fluent language, prompting debates both about their semantic understanding and, less prominently, about whether they can perform speech acts. This paper addresses the latter question, focusing on assertion. We argue that to be capable of assertion, an entity must meet two requirements: it must produce outputs with descriptive functions, and it must be capable of being sanctioned by agents with which it interacts. The second requirement arises from the (...) nature of assertion as a norm-governed social practice. Pre-trained large language models that have not been subject to fine-tuning fail to meet the first requirement. Language models that have been fine-tuned for ‘groundedness’ or ‘correctness’ may meet the first requirement, but fail the second. We also consider the significance of the point that AI systems can be used to generate proxy assertions on behalf of human agents. (shrink)

This paper considers the kind of introspection that large language models (LLMs) might be able to have. It argues that LLMs, while currently limited in their introspective capabilities, are not inherently unable to have such capabilities: they already model the world, including mental concepts, and already have some introspection-like capabilities. With deliberate training, LLMs may develop introspective capabilities. The paper proposes a method for such training for introspection, situates possible LLM introspection in the 'possible forms of introspection' framework proposed (...) by Kammerer and Frankish, and considers the ethical ramifications of introspection and self-report in AI systems. (shrink)

Many philosophers have argued that large language models (LLMs) subvert the traditional undergraduate philosophy paper. For the enthusiastic, LLMs merely subvert the traditional idea that students ought to write philosophy papers “entirely on their own.” For the more pessimistic, LLMs merely facilitate plagiarism. We believe that these controversies neglect a more basic crisis. We argue that, because one can, with minimal philosophical effort, use LLMs to produce outputs that at least “look like” good papers, many students will complete paper (...) assignments in a way that fails to develop their philosophical abilities. We argue that this problem exists even if students can produce better papers with AI and even if instructors can detect AI-generated content with decent reliability. But LLMs also create a pedagogical opportunity. We propose that instructors shift the emphasis of their assignments from philosophy papers to “LLM dialogues”: philosophical conversations between the student and an LLM. We describe our experience with using these types of assignments over the past several semesters. We argue that, far from undermining quality philosophical instruction, LLMs allow us to teach philosophy more effectively than was possible before. (shrink)

This article considers the results of a global survey into quality terminology, which suggested that quality professionals are not making use of their own standards. Discussion of quality is located in the context of partnership and networks.

I defend a claim, central to much work in psycholinguistics, that constructing mental representations of syntactic structures is a necessary step in language comprehension. Call such representations “mental phrase markers”. Several theorists in psycholinguistics, AI, and philosophy have cast doubt on the usefulness of positing MPMs. I examine their proposals and argue that they face major empirical and conceptual difficulties. My conclusions tell against the broader skepticism that persists in philosophy—e.g., in the embodied cognition literature —about the usefulness of (...) positing mental representations in psychological models. Using my discussion of sentence parsing as a case study, I propose several conditions on an appropriate appeal to mental representations. Finally, I point out that the familiar arguments from productivity, systematicity, and inferential coherence suffice to establish that MPMs themselves have a c.. (shrink)

Can large language models be considered intelligent? Arguments against this proposition often assume that genuine intelligence cannot exist without consciousness, understanding, or creative thinking. We discuss each of these roadblocks to machine intelligence and conclude that, in light of findings and conceptualizations in scientific research on these topics, none of them rule out the possibility of viewing current AI systems based on large language models as intelligent. We argue that consciousness is not relevant for AI, while creativity and (...) understanding should be considered functional traits that, in principle, can be implemented in a machine. Many arguments in circulation that claim current AI systems necessarily lack these traits either rely on human exceptionalism or mistaken understanding of human intelligence. Arguments that highlight alleged flaws in current systems—such as a lack of reliability, agency, or understanding—may be important, but they often obscure the many qualitative similarities between human cognition and current AI models. We further suggest that a critical examination of high-performance AI systems can serve as a mirror through which we can reflect on our own intelligence. Many arguments against the prospects of AI may prove disconcerting for naively optimistic assessments of capacities of the human mind. (shrink)

Elephant 2000 is a proposed programming language good for writing and verifying programs that interact with people (eg. transaction processing) or interact with programs belonging to other organizations (eg. electronic data interchange) 1. Communication inputs and outputs are in an I-O language whose sentences are meaningful speech acts identified in the language as questions, answers, offers, acceptances, declinations, requests, permissions and promises. 2. The correctness of programs is partly defined in terms of proper performance of the speech (...) acts. Answers should be truthful and responsive, and promises should be kept. Sentences of logic expressing these forms of correctness can be generated automatically from the form of the program. 3. Elephant aource programs may not need data structures, because they can refer directly to the past. Thus a program can say that an airline passenger has a reservation if he has made one and hasn't cancelled it. 4. Elephant programs themselves can be represented as sentences of logic. Their extensional properties follow from this representation without an intervening theory of programming or anything like Hoare axioms. 5. Elephant programs that interact non-trivially with the outside world can have both input-output specification, relating the programs inputs and outputs, and accomplishment specifications concerning what the program accomplishes in the world. These concepts are respectively generalizations of the philosophers' illocutionary and perlocutionary speech acts. 6. Programs that engage in commercial transactions assume obligations on behalf of their owners in exchange for obligations assumed by other entities. It may be part of the specification of an Elephant 2000 program that these obligations are exchanged as intended, and this too can be expressed by a logical sentence. 7. Human speech acts involve intelligence. Elephant 2000 is on the borderline of AI, but the article emphasizes the Elephant usages that do not require AI. (shrink)

The role of “craft language” in the process of teaching (learning) “Waza” (skill) will be discussed from the perspective of human intelligence.It may be said that the ultimate goal of learning “Waza” in any Japanese traditional performance is not the perfect reproduction of the teaching (learning) process of “Waza”. In fact, a special metaphorical language (“craft language”) is used, which has the effect of encouraging the learner to activate his creative imagination. It is through this activity that (...) the he learns his own “habitus” (“Kata”).It is suggested that, in considering the difference of function between natural human intelligence and artificial intelligence, attention should be paid to the imaginative activity of the learner as being an essential factor for mastering “Kata”. (shrink)

Large Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5’s legal reasoning and ChatGPT’s legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on (...) complaints written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5’s legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors’ decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT’s drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM’s legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct. (shrink)

Recently developed large language models (LLMs) perform surprisingly well in many language-related tasks, ranging from text correction or authentic chat experiences to the production of entirely new texts or even essays. It is natural to get the impression that LLMs know the meaning of natural language expressions and can use them productively. Recent scholarship, however, has questioned the validity of this impression, arguing that LLMs are ultimately incapable of understanding and producing meaningful texts. This paper develops a (...) more optimistic view. Drawing on classic externalist accounts of reference, it argues that LLM-generated texts meet the conditions of successful reference. This holds at least for proper names and so-called paradigm terms. The key insight here is that the LLM may inherit reference from its training-data through a reference-sustaining training mechanism. (shrink)

The integration of artificial intelligence (AI), particularly large language models (LLMs) like OpenAI’s ChatGPT, into clinical research could significantly enhance the informed consent process. This paper critically examines the ethical implications of employing LLMs to facilitate consent in clinical research. LLMs could offer considerable benefits, such as improving participant understanding and engagement, broadening participants’ access to the relevant information for informed consent and increasing the efficiency of consent procedures. However, these theoretical advantages are accompanied by ethical risks, including the (...) potential for misinformation, coercion and challenges in accountability. Given the complex nature of consent in clinical research, which involves both written documentation (in the form of participant information sheets and informed consent forms) and in-person conversations with a researcher, the use of LLMs raises significant concerns about the adequacy of existing regulatory frameworks. Institutional Review Boards (IRBs) will need to consider substantial reforms to accommodate the integration of LLM-based consent processes. We explore five potential models for LLM implementation, ranging from supplementary roles to complete replacements of current consent processes, and offer recommendations for researchers and IRBs to navigate the ethical landscape. Thus, we aim to provide practical recommendations to facilitate the ethical introduction of LLM-based consent in research settings by considering factors such as participant understanding, information accuracy, human oversight and types of LLM applications in clinical research consent. (shrink)