2 citations · 2 across the 5 of their papers we have counts for
5 papers
Reasoning Fine-Tuning Induces Persistent Latent Policy States
Abir Harrasse, Michael Lan, Hunar Batra +2
Reasoning-specialized language models show large performance gains over base models, yet the internal changes responsible for improved multi-step reasoning remain poorly understood…
Teaching People LLM's Errors and Getting it Right
Nathan Stringham, Fateme Hashemi Chaleshtori, Xinyuan Yan +3
People use large language models (LLMs) when they should not. This is partly because they see LLMs compose poems and answer intricate questions, so they understandably, but incorre…
BriefMe: A Legal NLP Benchmark for Assisting with Legal Briefs
Jesse Woo, Fateme Hashemi Chaleshtori, Ana Marasović +1
A core part of legal work that has been under-explored in Legal NLP is the writing and editing of legal briefs. This requires not only a thorough understanding of the law of a juri…
Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps
Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasović +1
When prompted to think step-by-step, language models (LMs) produce a chain of thought (CoT), a sequence of reasoning steps that the model supposedly used to produce its prediction.…
On Evaluating Explanation Utility for Human-AI Decision Making in NLP
Fateme Hashemi Chaleshtori, Atreya Ghosal, Alexander Gill +2
Is explainability a false promise? This debate has emerged from the insufficient evidence that explanations help people in situations they are introduced for. More human-centered,…