How Do AI Models Actually Think? - Laura Ruis

Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabilities, and whether these models primarily rely on retrieval or develop procedural knowledge.

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TOC

1. LLM Foundations and Learning

1.1 Scale and Learning in Language Models [00:00:00]

1.2 Procedural Knowledge vs Fact Retrieval [00:03:40]

1.3 Influence Functions and Model Analysis [00:07:40]

1.4 Role of Code in LLM Reasoning [00:11:10]

1.5 Semantic Understanding and Physical Grounding [00:19:30]

2. Reasoning Architectures and Measurement

2.1 Measuring Understanding and Reasoning in Language Models [00:23:10]

2.2 Formal vs Approximate Reasoning and Model Creativity [00:26:40]

2.3 Symbolic vs Subsymbolic Computation Debate [00:34:10]

2.4 Neural Network Architectures and Tensor Product Representations [00:40:50]

3. AI Agency and Risk Assessment

3.1 Agency and Goal-Directed Behavior in Language Models [00:45:10]

3.2 Defining and Measuring Agency in AI Systems [00:49:50]

3.3 Core Knowledge Systems and Agency Detection [00:54:40]

3.4 Language Models as Agent Models and Simulator Theory [01:03:20]

3.5 AI Safety and Societal Control Mechanisms [01:07:10]

3.6 Evolution of AI Capabilities and Emergent Risks [01:14:20]

REFS:

[00:01:10] Procedural Knowledge in Pretraining & LLM Reasoning

Ruis et al., 2024

arxiv.org/abs/2411.12580

[00:03:50] EK-FAC Influence Functions in Large LMs

Grosse et al., 2023

arxiv.org/abs/2308.03296

[00:13:05] Surfaces and Essences: Analogy as the Core of Cognition

Hofstadter & Sander

amazon.com/Surfaces-Essences-Analogy-Fuel-Thin…

[00:13:45] Wittgenstein on Language Games

plato.stanford.edu/entries/wittgenstein

[00:14:30] Montague Semantics for Natural Language

plato.stanford.edu/entries/montague-semantics

[00:19:35] The Chinese Room Argument

David Cole

plato.stanford.edu/entries/chinese-room

[00:19:55] ARC: Abstraction and Reasoning Corpus

François Chollet

arxiv.org/abs/1911.01547

[00:24:20] Systematic Generalization in Neural Nets

Lake & Baroni, 2023

nature.com/articles/s41586-023-06668-3

[00:27:40] Open-Endedness & Creativity in AI

Tim Rocktäschel

arxiv.org/html/2406.04268v1

[00:30:50] Fodor & Pylyshyn on Connectionism

sciencedirect.com/science/article/abs/pii/0010…

[00:31:30] Tensor Product Representations

Smolensky, 1990

sciencedirect.com/science/article/abs/pii/0004…

[00:35:50] DreamCoder: Wake-Sleep Program Synthesis

Kevin Ellis et al.

courses.cs.washington.edu/courses/cse599j1/22s…

[00:36:30] Compositional Generalization Benchmarks

Ruis, Lake et al., 2022

arxiv.org/pdf/2202.10745

[00:40:30] RNNs & Tensor Products

McCoy et al., 2018

arxiv.org/abs/1812.08718

[00:46:10] Formal Causal Definition of Agency

Kenton et al.

arxiv.org/pdf/2208.08345v2

[00:48:40] Agency in Language Models

Sumers et al.

arxiv.org/abs/2309.02427

[00:55:20] Heider & Simmel’s Moving Shapes Experiment

nature.com/articles/s41598-024-65532-0

[01:00:40] Language Models as Agent Models

Jacob Andreas, 2022

arxiv.org/abs/2212.01681

[01:13:35] Pragmatic Understanding in LLMs

Ruis et al.

arxiv.org/abs/2210.14986