Machine Learning Street Talk (MLST)
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.
SPONSOR MESSAGES:
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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