139 citations · 462 across the 39 of their papers we have counts for
5 papers · 2 filters
Minimization of Boolean Complexity in In-Context Concept Learning
Leroy Z. Wang, R. Thomas McCoy, Shane Steinert-Threlkeld
What factors contribute to the relative success and corresponding difficulties of in-context learning for Large Language Models (LLMs)? Drawing on insights from the literature on h…
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1
R. Thomas McCoy, Shunyu Yao, Dan Friedman +2
In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in ne…
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning
Akshara Prabhakar, Thomas L. Griffiths, R. Thomas McCoy
Chain-of-Thought (CoT) prompting has been shown to enhance the multi-step reasoning capabilities of Large Language Models (LLMs). However, debates persist about whether LLMs exhibi…
modeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models
Nathan A. Chi, Teodor Malchev, Riley Kong +5
We introduce modeLing, a novel benchmark of Linguistics Olympiad-style puzzles which tests few-shot reasoning in AI systems. Solving these puzzles necessitates inferring aspects of…
Is In-Context Learning a Type of Error-Driven Learning? Evidence from the Inverse Frequency Effect in Structural Priming
Zhenghao Zhou, Robert Frank, R. Thomas McCoy
Large language models (LLMs) have shown the emergent capability of in-context learning (ICL). One line of research has claimed that ICL is functionally equivalent to gradient desce…