Language Writ Large: LLMs, ChatGPT, Meaning and Understanding (Harnad)

Stevan Harnad. UQÀM, McGill , 25 January 2004

ABSTRACT: Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, etc.). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. It has even driven some of us to conclude that it actually understands. It’s not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign “biases” — convergent constraints that emerge at LLM-scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM-scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to integrate its words with their referents in the real world and to integrate its propositions with their meanings. These benign biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the “mirroring” of language production and comprehension, (4) iconicity in propositions at LLM-scale, (5) computational counterparts of human “categorical perception” in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought.

Stevan Harnad is Professor of psychology and cognitive science at UQÀM. His research is on category-learning, symbol-grounding, language-evolution, and Turing-Testing

Bonnasse-Gahot, L., & Nadal, J. P. (2022). Categorical perception: a groundwork for deep learning. Neural Computation, 34(2), 437-475.

Harnad, S. (2024). Language Writ Large: LLMs, ChatGPT, Grounding, Meaning and Understanding. arXiv preprint arXiv:2402.02243.

Harnad, S. (2012). From sensorimotor categories and pantomime to grounded symbols and propositions In: Gibson, KR & Tallerman, M (eds.) The Oxford Handbook of Language Evolution 387-392.

Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence. In: Epstein, R, Roberts, Gary & Beber, G. (eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer, pp. 23-66.

Thériault, C., Pérez-Gay, F., Rivas, D., & Harnad, S. (2018). Learning-induced categorical perception in a neural network model. arXiv preprint arXiv:1805.04567.

Vincent‐Lamarre, P; Blondin-Massé, A; Lopes, M; Lord, M; Marcotte, O; & Harnad, S (2016). The latent structure of dictionaries. Topics in Cognitive Science 8(3): 625-659.

Pérez-Gay Juárez, F., Sicotte, T., Thériault, C., & Harnad, S. (2019). Category learning can alter perception and its neural correlates. PloS one, 14(12), e0226000.

Mechanistic Explanation in Deep Learning (Millière)

Raphaël Millière, Philosophy, Macquarie University, 14 September, 2024

VIDEO

Abstract: Deep neural networks such as large language models (LLMs) have achieved impressive performance across almost every domain of natural language processing, but there remains substantial debate about which cognitive capabilities can be ascribed to these models. Drawing inspiration from mechanistic explanations in life sciences, the nascent field of “mechanistic interpretability” seeks to reverse-engineer human-interpretable features to explain how LLMs process information. This raises some questions: (1) Are causal claims about neural network components, based on coarse intervention methods (such as “activation patching”), genuine mechanistic explanations? (2) Does the focus on human-interpretable features risk imposing anthropomorphic assumptions? My answer will be “yes” to (1) and “no” to (2), closing with a discussion of some ongoing challenges.

Raphael Millière is Lecturer in Philosophy of Artificial Intelligence at Macquarie University in Sydney, Australia. His interests are in the philosophy of artificial intelligence, cognitive science, and mind, particularly in understanding artificial neural networks based on deep learning architectures such as Large Language Models. He has investigated syntactic knowledge, semantic competence, compositionality, variable binding, and grounding.

Elhage, N., et al. (2021). A mathematical framework for transformer circuits. Transformer Circuits Thread

Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about Mechanisms. Philosophy of Science, 67(1), 1–25.

Millière, R. (2023). The Alignment Problem in Context. arXiv preprint arXiv:2311.02147

Mollo, D. C., & Millière, R. (2023). The vector grounding problem. arXiv preprint arXiv:2304.01481

Yousefi, S., et al. (2023). In-Context Learning in Large Language Models: A Neuroscience-inspired Analysis of Representations. arXiv preprint arXiv:2310.00313.

LLMs, Patterns, and Understanding (Durt)

Christof Durt , Philosophy, U. Heidelberg, 30-Nov 2023

VIDEO

ABSTRACT: It is widely known that the performance of LLMs is contingent on their being trained with very large text corpora. But what in the text corpora allows LLMs to extract the parameters that enable them to produce text that sounds as if it had been written by an understanding being? In my presentation, I argue that the text corpora reflect not just “language” but language use. Language use is permeated with patterns, and the statistical contours of the patterns of written language use are modelled by LLMs. LLMs do not model understanding directly, but statistical patterns that correlate with patterns of language use. Although the recombination of statistical patterns does not require understanding, it enables the production of novel text that continues a prompt and conforms to patterns of language use, and thus can make sense to humans.

Christoph Durt is a philosophical and interdisciplinary researcher at Heidelberg university. He investigates the human mind and its relation to technology, especially AI. Going beyond the usual side-to-side comparison of artificial and human intelligence, he studies the multidimensional interplay between the two. This involves the study of human experience and language, as well as the relation between them. If you would like to join an international online exchange on these issues, please check the “courses and lectures” section on his website.

Durt, Christoph, Tom Froese, and Thomas Fuchs. preprint. “Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use.”

Durt, Christoph. 2023. “The Digital Transformation of Human Orientation: An Inquiry into the Dawn of a New Era” Winner of the $10.000 HFPO Essay Prize.

Durt, Christoph. 2022. “Artificial Intelligence and Its Integration into the Human Lifeworld.” In The Cambridge Handbook of Responsible Artificial Intelligence, Cambridge University Press.

Durt, Christoph. 2020. “The Computation of Bodily, Embodied, and Virtual Reality” Winner of the Essay Prize “What Can Corporality as a Constitutive Condition of Experience (Still) Mean in the Digital Age?”Phänomenologische Forschungen, no. 2: 25–39.

Robotic Grounding and LLMs: Advancements and Challenges (Kennington)

Casey Kennington , Computer Science, Boise State, 09-Nov 2023

VIDEO

ABSTRACT: Large Language Models (LLMs) are p rimarily trained using large amounts of text, but there have also been noteworthy advancements in incorporating vision and other sensory information into LLMs. Does that mean LLMs are ready for embodied agents such as robots? While there have been important advancements, technical and theoretical challenges remain including use of closed language models like ChatGPT, model size requirements, data size requirements, speed requirements, representing the physical world, and updating the model with information about the world in real time. In this talk, I explain recent advance on incorporating LLMs into robot platforms, challenges, and opportunities for future work.

Casey Kennington is associate professor in the Department of Computer Science at Boise State University where he does research on spoken dialogue systems on embodied platforms. His long-term research goal is to understand what it means for humans to understand, represent, and produce language. His National Science Foundation CAREER award focuses on enriching small language models with multimodal information such as vision and emotion for interactive learning on robotic platforms. Kennington obtained his PhD in Linguistics from Bielefeld University, Germany.

Josue Torres-Foncesca, Catherine Henry, Casey Kennington. Symbol and Communicative Grounding through Object Permanence with a Mobile Robot. In Proceedings of SigDial, 2022.

Clayton Fields and Casey Kennington. Vision Language Transformers: A Survey. arXiv, 2023.

Casey Kennington. Enriching Language Models with Visually-grounded Word Vectors and the Lancaster Sensorimotor Norms. In Proceedings of CoNLL, 2021

Casey Kennington. On the Computational Modeling of Meaning: Embodied Cognition Intertwined with Emotion. arXiv, 2023.

Machine Psychology (Schulz)

Eric Schulz , MPI Tuebingen, 02-Nov 2023

VIDEO

ABSTRACT: Large language models are on the cusp of transforming society while they permeate into many applications. Understanding how they work is, therefore, of great value. We propose to use insights and tools from psychology to study and better understand these models. Psychology can add to our understanding of LLMs and provide a new toolkit for explaining LLMs by providing theoretical concepts, experimental designs, and computational analysis approaches. This can lead to a machine psychology for foundation models that focuses on computational insights and precise experimental comparisons instead of performance measures alone. I will showcase the utility of this approach by showing how current LLMs behave across a variety of cognitive tasks, as well as how one can make them more human-like by fine-tuning on psychological data directly.

Eric Schulz, Max-Planck Research Group Leader, Tuebingen University works on the building blocks of intelligence using a mixture of computational, cognitive, and neuroscientific methods. He has worked with Maarten Speekenbrink on generalization as function learning and Sam Gershman and Josh Tenenbaum.

Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences, 120(6), e2218523120

Akata, E., Schulz, L., Coda-Forno, J., Oh, S. J., Bethge, M., & Schulz, E. (2023). Playing repeated games with Large Language Models. arXiv preprint arXiv:2305.16867.

Allen, K. R., Brändle, F., Botvinick, M., Fan, J., Gershman, S. J., Griffiths, T. L., … & Schulz, E. (2023). Using Games to Understand the Mind

Binz, M., & Schulz, E. (2023). Turning large language models into cognitive models. arXiv preprint.

The Debate Over “Understanding” in AI’s Large Language Models (Mitchell)

Melanie Mitchell , Santa Fe Institute, 19-Oct

VIDEO

ABSTRACT: I will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said — in any important sense — to “understand” language and the physical and social situations language encodes. I will describe arguments that have been made for and against such understanding, and, more generally, will discuss what methods can be used to fairly evaluate understanding and intelligence in AI systems. I will conclude with key questions for the broader sciences of intelligence that have arisen in light of these discussions.

Melanie Mitchell is Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction and analogy-making in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her 2009 book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award, and her 2019 book Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux) is a finalist for the 2023 Cosmos Prize for Scientific Writing.

Mitchell, M. (2023). How do we know how smart AI systems are? Science, 381(6654), adj5957.

Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI’s large language models. Proceedings of the National Academy of Sciences, 120(13), e2215907120.

Millhouse, T., Moses, M., & Mitchell, M. (2022). Embodied, Situated, and Grounded Intelligence: Implications for AI. arXiv preprint arXiv:2210.13589.

Rethinking the Physical Symbol Systems Hypothesis (Rosenbloom)

Paul Rosenbloom , Computer Science, USC, 12-Oct 2023

VIDEO

ABSTRACT: It is now more than a half-century since the Physical Symbol Systems Hypothesis (PSSH) was first articulated as an empirical hypothesis. More recent evidence from work with neural networks and cognitive architectures has weakened it, but it has not yet been replaced in any satisfactory manner. Based on a rethinking of the nature of computational symbols – as atoms or placeholders – and thus also of the systems in which they participate, a hybrid approach is introduced that responds to these challenges while also helping to bridge the gap between symbolic and neural approaches, resulting in two new hypotheses, one – the Hybrid Symbol Systems Hypothesis (HSSH) – that is to replace the PSSH and the other focused more directly on cognitive architectures. This overall approach has been inspired by how hybrid symbol systems are central in the Common Model of Cognition and the Sigma cognitive architectures, both of which will be introduced – along with the general notion of a cognitive architecture – via “flashbacks” during the presentation.

Paul S. Rosenbloom is Professor Emeritus of Computer Science in the Viterbi School of Engineering at the University of Southern California (USC). His research has focused on cognitive architectures (models of the fixed structures and processes that together yield a mind), such as Soar and Sigma; the Common Model of Cognition (a partial consensus about the structure of a human-like mind); dichotomic maps (structuring the space of technologies underlying AI and cognitive science); “essential” definitions of key concepts in AI and cognitive science (such as intelligence, theories, symbols, and architectures); and the relational model of computing as a great scientific domain (akin to the physical, life and social sciences).

Rosenbloom, P. S. (2023). Rethinking the Physical Symbol Systems Hypothesis. In Proceedings of the 16^th International Conference on Artificial General Intelligence (pp. 207-216). Cham, Switzerland: Springer.

Laird, J. E., Lebiere, C. & Rosenbloom, P. S. (2017). A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Magazine, 38, 13-26.

Rosenbloom, P. S., Demski, A. & Ustun, V. (2016). The Sigma cognitive architecture and system: Towards functionally elegant grand unification. Journal of Artificial General Intelligence, 7, 1-103.

Rosenbloom, P. S., Demski, A. & Ustun, V. (2016). Rethinking Sigma’s graphical architecture: An extension to neural networks. Proceedings of the 9^th Conference on Artificial General Intelligence (pp. 84-94).

Grounding in Large Language Models: Functional Ontologies for AI (Mollo)

Dimitri Coelho Mollo. Philosophy of AI, Umeå University, 21 sept 2023

VIDEO

ABSTRACT: I will describe joint work with Raphaël Millière, arguing that language grounding (but not language understanding) is possible in some current Large Language Models (LLMs). This does not mean, h that the way language grounding works in LLMs is similar to how grounding works in humans. The differences open up two options: narrowing the notion of grounding to only the phenomenon in humans; or pluralism about grounding, extending the notion more broadly to systems that fulfil the requirements for intrinsic content. Pluralism invites applying recent work in comparative and cognitive psychology to AI, especially the search for appropriate ontologies to account for cognition and intelligence. This can help us better understand the capabilities and limitations of current AI systems, as well as potential ways forward.

Dimitri Coelho Mollo is Assistant Professor with focus in Philosophy of Artificial Intelligence at the Department of Historical, Philosophical and Religious Studies, at Umeå University, Sweden, and focus area coordinator at TAIGA (Centre for Transdisciplinary AI), for the area ‘Understanding and Explaining Artificial Intelligence’. I am also an external Principal Investigator at the Science of Intelligence Cluster, in Berlin, Germany. My research focuses on foundational and epistemic questions within artificial intelligence and cognitive science, looking for ways to improve our understanding of mind, cognition, and intelligence in biological and artificial systems. My work often intersects issues in Ethics of Artificial Intelligence, Philosophy of Computing, and Philosophy of Biology.

Coelho Mollo and Millière (2023), The Vector Grounding Problem

Francken, Slors, Craver (2022), Cognitive ontology and the search for neural mechanisms: three foundational problems

LLMs are impressive but we still need grounding to explain human cognition (Bergen)

Benjamin Bergen, Cognitive Science, UCSD, 14 sept 2023

VIDEO

ABSTRACT: Human cognitive capacities are often explained as resulting from grounded, embodied, or situated learning. But Large Language Models, which only learn on the basis of word co-occurrence statistics, now rival human performance in a variety of tasks that would seem to require these very capacities. This raises the question: is grounding still necessary to explain human cognition? I report on studies addressing three aspects of human cognition: Theory of Mind, Affordances, and Situation Models. In each case, we run both human and LLM participants on the same task and ask how much of the variance in human behavior is explained by the LLMs. As it turns out, in all cases, human behavior is not fully explained by the LLMs. This entails that, at least for now, we need grounding (or, more accurately, something that goes beyond statistical language learning) to explain these aspects of human cognition. I’ll conclude by asking but not answering a number of questions, like, How long will this remain the case? What are the right criteria for an LLM that serves as a proxy for human statistical language learning? and, How could one tell conclusively whether LLMs have human-like intelligence?

Ben Bergen is Professor of Cognitive Science at UC San Diego, where he directs the Language and Cognition Lab. His research focuses on language processing and production with a special interest in meaning. He’s also the author of ‘Louder than Words: The New Science of How the Mind Makes Meaning‘ and ‘What the F: What Swearing Reveals about Our Language, Our Brains, and Ourselves.’

Trott, S., Jones, C., Chang, T., Michaelov, J., & Bergen, B. (2023). Do Large Language Models know what humans know? Cognitive Science 47(7): e13309.

Chang, T. & B. Bergen (2023). Language Model Behavior: A Comprehensive Survey. Computational Linguistics.

Michaelov, J., S. Coulson, & B. Bergen (2023). Can Peanuts Fall in Love with Distributional Semantics? Proceedings of the 45th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Jones, C., Chang, T., Coulson, S., Michaelov, J., Trott, T., & Bergen, B. (2022). Distributional Semantics Still Can’t Account for Affordances. Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Conscious processing, inductive biases and generalization in deep learning (Bengio)

Yoshua Bengio, MILA, Université de Montréal 17 feb 2023

VIDEO

Abstract: Humans are very good at “out-of-distribution” generalization (compared to current AI systems). It would be useful to determine the inductive biases they exploit and translate them into machine-language architectures, training frameworks and experiments. I will discuss several of these hypothesized inductive biases. Many exploit notions in causality and connect abstractions in representation learning (perception and interpretation) with reinforcement learning (abstract actions). Systematic generalizations may arise from efficient factorization of knowledge into recomposable pieces. This is partly related to symbolic AI (aas seen in the errors and limitations of reasoning in humans, as well as in our ability to learn to do this at scale, with distributed representations and efficient search). Sparsity of the causal graph and locality of interventions — observable in the structure of sentences — may reduce the computational complexity of both inference (including planning) and learning. This may be why evolution incorporated this as “consciousness.” I will also suggest some open research questions to stimulate further research and collaborations.

Yoshua Bengio, Professor, University of Montreal, founder and scientific director of Mila – Institut québécois d’AI, and co-director CIFAR’s Machine Learning, Biological Learning program as a Senior Fellow. He also serves as scientific director of IVADO.

Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., … & VanRullen, R. (2023). Consciousness in artificial intelligence: insights from the science of consciousness. arXiv preprint arXiv:2308.08708.

Zador, A., Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., … & Tsao, D. (2023). Catalyzing next-generation artificial intelligence through neuroai. Nature Communications, 14(1), 1597.