9 citations · 12 across the 4 of their papers we have counts for
5 papers
Language Models that Play Chess and Explain Their Moves
Adithya Bhaskar, Jeffrey Cheng, Danqi Chen
Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play. On the other hand, language models (LMs) can generate plausi…
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?
Jiefu Ou, William Gantt Walden, Kate Sanders +13
A core part of scientific peer review involves providing expert critiques that directly assess the scientific claims a paper makes. While it is now possible to automatically genera…
Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering
William Jurayj, Jeffrey Cheng, Benjamin Van Durme
Scaling the test-time compute of large language models has demonstrated impressive performance on reasoning benchmarks. However, existing evaluations of test-time scaling make the…
Compressed Chain of Thought: Efficient Reasoning Through Dense Representations
Jeffrey Cheng, Benjamin Van Durme
Chain-of-thought (CoT) decoding enables language models to improve reasoning performance at the cost of high generation latency in decoding. Recent proposals have explored variants…
Dated Data: Tracing Knowledge Cutoffs in Large Language Models
Jeffrey Cheng, Marc Marone, Orion Weller +3
Released Large Language Models (LLMs) are often paired with a claimed knowledge cutoff date, or the dates at which training data was gathered. Such information is crucial for appli…