44 citations · 99 across the 7 of their papers we have counts for
5 papers · 1 filter
DiscoveryBench: Towards Data-Driven Discovery with Large Language Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal +7
Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely fro…
Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance
Yao Fu, Litu Ou, Mingyu Chen +3
As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-…
Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
Yao Fu, Hao Peng, Tushar Khot +1
We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this…
Specializing Smaller Language Models towards Multi-Step Reasoning
Yao Fu, Hao Peng, Litu Ou +2
The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-sca…
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts
Daniel Khashabi, Shane Lyu, Sewon Min +8
Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the fe…