99 citations · 238 across the 31 of their papers we have counts for
6 papers · 1 filter
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…
Eliciting the Priors of Large Language Models using Iterated In-Context Learning
Jian-Qiao Zhu, Thomas L. Griffiths
As Large Language Models (LLMs) are increasingly deployed in real-world settings, understanding the knowledge they implicitly use when making decisions is critical. One way to capt…
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
Ryan Liu, Theodore R. Sumers, Ishita Dasgupta +1
In day-to-day communication, people often approximate the truth - for example, rounding the time or omitting details - in order to be maximally helpful to the listener. How do larg…
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…
Large language models predict human sensory judgments across six modalities
Raja Marjieh, Ilia Sucholutsky, Pol van Rijn +2
Determining the extent to which the perceptual world can be recovered from language is a longstanding problem in philosophy and cognitive science. We show that state-of-the-art lar…