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

https://arxiv.org/abs/2411.12580

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

Grosse et al., 2023

https://arxiv.org/abs/2308.03296

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

Hofstadter & Sander

https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475

[00:13:45 ] Wittgenstein on Language Games

https://plato.stanford.edu/entries/wittgenstein/

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

https://plato.stanford.edu/entries/montague-semantics/

[00:19:35 ] The Chinese Room Argument

David Cole

https://plato.stanford.edu/entries/chinese-room/

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

François Chollet

https://arxiv.org/abs/1911.01547

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

Lake & Baroni, 2023

https://www.nature.com/articles/s41586-023-06668-3

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

Tim Rocktäschel

https://arxiv.org/html/2406.04268v1

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

https://www.sciencedirect.com/science/article/abs/pii/0010027788900315

[00:31:30 ] Tensor Product Representations

Smolensky, 1990

https://www.sciencedirect.com/science/article/abs/pii/000437029090007M

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

Kevin Ellis et al.

https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf

[00:36:30 ] Compositional Generalization Benchmarks

Ruis, Lake et al., 2022

https://arxiv.org/pdf/2202.10745

[00:40:30 ] RNNs & Tensor Products

McCoy et al., 2018

https://arxiv.org/abs/1812.08718

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

Kenton et al.

https://arxiv.org/pdf/2208.08345v2

[00:48:40 ] Agency in Language Models

Sumers et al.

https://arxiv.org/abs/2309.02427

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

https://www.nature.com/articles/s41598-024-65532-0

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

Jacob Andreas, 2022

https://arxiv.org/abs/2212.01681

[01:13:35 ] Pragmatic Understanding in LLMs

Ruis et al.

https://arxiv.org/abs/2210.14986