565 citations · 581 across the 9 of their papers we have counts for
4 papers · 1 filter
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
Peter Hase, Mohit Bansal, Peter Clark +1
How can we train models to perform well on hard test data when hard training data is by definition difficult to label correctly? This question has been termed the scalable oversigh…
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz +3
Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into accoun…
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
Swarnadeep Saha, Peter Hase, Mohit Bansal
A hallmark property of explainable AI models is the ability to teach other agents, communicating knowledge of how to perform a task. While Large Language Models perform complex rea…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…