3 citations · 3 across the 2 of their papers we have counts for
7 papers
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…
Distilling Symbolic Priors for Concept Learning into Neural Networks
Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths
Humans can learn new concepts from a small number of examples by drawing on their inductive biases. These inductive biases have previously been captured by using Bayesian models de…
Bayes in the age of intelligent machines
Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant +1
The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Baye…
Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve
R. Thomas McCoy, Shunyu Yao, Dan Friedman +2
The widespread adoption of large language models (LLMs) makes it important to recognize their strengths and limitations. We argue that in order to develop a holistic understanding…
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks
R. Thomas McCoy, Thomas L. Griffiths
Humans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science. Bayesian mo…