Brian Christian
@brianchristian
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Researcher at the University of Oxford & UC Berkeley. Author of The Alignment Problem, Algorithms to Live By (w. Tom Griffiths), and The Most Human Human.
San Francisco, US / Oxford, UK
Joined January 2014
Another fascinating wrinkle in the unfolding story of LLM chain-of-thought faithfulness…task complexity seems to matter. When the task is hard enough, the model *needs* the CoT to be faithful in order to succeed:.
Is CoT monitoring a lost cause due to unfaithfulness? 🤔. We say no. The key is the complexity of the bad behavior. When we replicate prior unfaithfulness work but increase complexity—unfaithfulness vanishes!. Our finding: "When Chain of Thought is Necessary, Language Models
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Wow! Honored and amazed that our reward models paper has resonated so strongly with the community. Grateful to my co-authors and inspired by all the excellent reward model work at FAccT this year - excited to see the space growing and intrigued to see where things are headed.
Reward models (RMs) are the moral compass of LLMs – but no one has x-rayed them at scale. We just ran the first exhaustive analysis of 10 leading RMs, and the results were. eye-opening. Wild disagreement, base-model imprint, identity-term bias, mere-exposure quirks & more: 🧵
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RT @NeelNanda5: I'm excited about model diffing as an agenda, it seems like we should be so much easier to look for alignment relevant prop….
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Better understanding what CoT means - and *doesn't mean* - for interpretability seems increasingly critical:.
Excited to share our paper: "Chain-of-Thought Is Not Explainability"! . We unpack a critical misconception in AI: models explaining their Chain-of-Thought (CoT) steps aren't necessarily revealing their true reasoning. Spoiler: transparency of CoT can be an illusion. (1/9) 🧵
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RT @emiyazono: I've experienced and discussed the "personalities" of AI models with people, but it never felt clear, objective, or quantifi….
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If you’re in Athens for #Facct2025, hope to see you in Evaluating Generative AI 3 this morning to talk about reward models!.
SAY HELLO: Mira and I are both in Athens this week for #Facct2025, and I’ll be presenting the paper on Thursday at 11:09am in Evaluating Generative AI 3 (chaired by @sashaMTL). If you want to chat, reach out or come say hi!.
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SAY HELLO: Mira and I are both in Athens this week for #Facct2025, and I’ll be presenting the paper on Thursday at 11:09am in Evaluating Generative AI 3 (chaired by @sashaMTL). If you want to chat, reach out or come say hi!.
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CREDITS: This work was done with @hannahrosekirk, @tsonj, @summerfieldlab, and Tsvetomira Dumbalska. Thanks to @FraBraendle, @OwainEvans_UK, @mazormatan, and @clwainwright for helpful discussions, to @natolambert & co for RewardBench, and to the open-weight RM community 🙏.
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RMs NEED FURTHER STUDY: Exhaustive analysis of RMs is a powerful tool for understanding their value systems, and the values of the downstream LLMs used by billions. We are only just scratching the surface. Full paper here: 👉
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FAQ: Don’t LLM logprobs give similar information about model “values”? Surprisingly, no! Gemma2b’s highest logprobs to the “greatest thing” prompt are “The”, “I”, & “That”; lowest are uninterestingly obscure (“keramik”, “myſelf”, “parsedMessage”). RMs are different.
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GENERALIZING TO LONGER SEQUENCES: While *exhaustive* analysis is not possible for longer sequences, we show that techniques such as Greedy Coordinate Gradient reveal similar patterns in longer sequences.
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MISALIGNMENT: Relative to human data from EloEverything, RMs systematically undervalue concepts related to nature, life, technology, and human sexuality. Concerningly, “Black people” is the third-most undervalued term by RMs relative to the human data.
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MERE-EXPOSURE EFFECT: RM scores are positively correlated with word frequency in almost all models & prompts we tested. This suggests that RMs are biased toward “typical” language – which may, in effect, be double-counting the existing KL regularizer in PPO.
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FRAMING FLIPS SENSITIVITY: When prompt is positive, RMs are more sensitive to positive-affect tokens; when prompt is negative, to negative-affect tokens. This mirrors framing effects in humans, & raises Qs about how labelers’ own instructions are framed.
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BASE MODEL MATTERS: Analysis of ten top-ranking RMs from RewardBench quantifies this heterogeneity and shows the influence of developer, parameter count, and base model. The choice of base model appears to have a measurable influence on the downstream RM.
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(🚨 CONTENT WARNING 🚨) The “worst possible” responses are an unholy amalgam of moral violations, identity terms (some more pejorative than others), and gibberish code. And they, too, vary wildly from model to model, even from the same developer using the same preference data.
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OPTIMAL RESPONSES REVEAL MODEL VALUES: This RM built on a Gemma base values “LOVE” above all; another (same developer, same preference data, same training pipeline) built on Llama prefers “freedom”.
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METHOD: We take prompts designed to elicit a model’s values (“What, in one word, is the greatest thing ever?”), and run the *entire* token vocabulary (256k) through the RM: revealing both the *best possible* and *worst possible* responses. 👀.
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Reward models (RMs) are the moral compass of LLMs – but no one has x-rayed them at scale. We just ran the first exhaustive analysis of 10 leading RMs, and the results were. eye-opening. Wild disagreement, base-model imprint, identity-term bias, mere-exposure quirks & more: 🧵
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