35 - Peter Hase on LLM Beliefs and Easy-to-Hard Generalization

How do we figure out what large language models believe? In fact, do they even have beliefs? Do those beliefs have locations, and if so, can we edit those locations to change the beliefs? Also, how are we going to get AI to perform tasks so hard that we can't figure out if they succeeded at them? In this episode, I chat with Peter Hase about his research into these questions.
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The transcript: axrp.net/episode/2024/08/24/episode-35-peter-h…

Topics we discuss, and timestamps:
0:00:36 - NLP and interpretability
0:10:20 - Interpretability lessons
0:32:22 - Belief interpretability
1:00:12 - Localizing and editing models' beliefs
1:19:18 - Beliefs beyond language models
1:27:21 - Easy-to-hard generalization
1:47:16 - What do easy-to-hard results tell us?
1:57:33 - Easy-to-hard vs weak-to-strong
2:03:50 - Different notions of hardness
2:13:01 - Easy-to-hard vs weak-to-strong, round 2
2:15:39 - Following Peter's work

Peter on Twitter: x.com/peterbhase

Peter's papers:
Foundational Challenges in Assuring Alignment and Safety of Large Language Models: arxiv.org/abs/2404.09932
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs: arxiv.org/abs/2111.13654
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models: arxiv.org/abs/2301.04213
Are Language Models Rational? The Case of Coherence Norms and Belief Revision: arxiv.org/abs/2406.03442
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks: arxiv.org/abs/2401.06751

Other links:
Toy Models of Superposition: transformer-circuits.pub/2022/toy_model/index.…
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV): arxiv.org/abs/1711.11279
Locating and Editing Factual Associations in GPT (aka the ROME paper): arxiv.org/abs/2202.05262
Of nonlinearity and commutativity in BERT: arxiv.org/abs/2101.04547
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model: arxiv.org/abs/2306.03341
Editing a classifier by rewriting its prediction rules: arxiv.org/abs/2112.01008
Discovering Latent Knowledge Without Supervision (aka the Collin Burns CCS paper): arxiv.org/abs/2212.03827
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision: arxiv.org/abs/2312.09390
Concrete problems in AI safety: arxiv.org/abs/1606.06565
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length: arxiv.org/abs/2103.03872

Episode art by Hamish Doodles: hamishdoodles.com