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Eric J Michaud

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

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2

TMLR Journal 2025 Journal Article

Open Problems in Mechanistic Interpretability

  • Lee Sharkey
  • Bilal Chughtai
  • Joshua Batson
  • Jack Lindsey
  • Jeffrey Wu
  • Lucius Bushnaq
  • Nicholas Goldowsky-Dill
  • Stefan Heimersheim

Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to reveal deeper insights; we must figure out how best to apply our methods in pursuit of specific goals; and the field must grapple with socio-technical challenges that influence and are influenced by our work. This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing.

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TMLR Journal 2023 Journal Article

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

  • Stephen Casper
  • Xander Davies
  • Claudia Shi
  • Thomas Krendl Gilbert
  • Jérémy Scheurer
  • Javier Rando
  • Rachel Freedman
  • Tomek Korbak

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-layered approach to the development of safer AI systems.

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