5 citations · 6 across the 5 of their papers we have counts for
6 papers
Training LLMs for Honesty via Confessions
Manas Joglekar, Jeremy Chen, Gabriel Wu +4
Large language models (LLMs) can be dishonest when reporting on their actions and beliefs -- for example, they may overstate their confidence in factual claims or cover up evidence…
Testing Tensor Products of Algebraic Codes
Sumegha Garg, Madhu Sudan, Gabriel Wu
Motivated by recent advances in locally testable codes and quantum LDPCs based on robust testability of tensor product codes, we explore the local testability of tensor products of…
Estimating the Probabilities of Rare Outputs in Language Models
Gabriel Wu, Jacob Hilton
We consider the problem of low probability estimation: given a machine learning model and a formally-specified input distribution, how can we estimate the probability of a binary p…
Towards Understanding Human Emotional Fluctuations with Sparse Check-In Data
Sagar Paresh Shah, Ga Wu, Sean W. Kortschot +1
Data sparsity is a key challenge limiting the power of AI tools across various domains. The problem is especially pronounced in domains that require active user input rather than m…
Harvard Undergraduate Survey on Generative AI
Shikoh Hirabayashi, Rishab Jain, Nikola Jurković +1
How has generative AI impacted the experiences of college students? We study the influence of AI on the study habits, class choices, and career prospects of Harvard undergraduates…
Within-basket Recommendation via Neural Pattern Associator
Kai Luo, Tianshu Shen, Lan Yao +7
Within-basket recommendation (WBR) refers to the task of recommending items to the end of completing a non-empty shopping basket during a shopping session. While the latest innovat…