52 citations · 57 across the 14 of their papers we have counts for
22 papers · 1 filter
LM Agents for Coordinating Multi-User Information Gathering
Harsh Jhamtani, Jacob Andreas, Benjamin Van Durme
This paper introduces PeopleJoin, a benchmark for evaluating LM-mediated collaborative problem solving. Given a user request, PeopleJoin agents must identify teammates who might be…
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
Abhinav Joshi, Shaswati Saha, Divyaksh Shukla +4
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vas…
Steering Large Language Models between Code Execution and Textual Reasoning
Yongchao Chen, Harsh Jhamtani, Srinagesh Sharma +2
While a lot of recent research focuses on enhancing the textual reasoning capabilities of Large Language Models (LLMs) by optimizing the multi-agent framework or reasoning chains,…
Learning to Retrieve Iteratively for In-Context Learning
Yunmo Chen, Tongfei Chen, Harsh Jhamtani +4
We introduce iterative retrieval, a novel framework that empowers retrievers to make iterative decisions through policy optimization. Finding an optimal portfolio of retrieved item…
Interpreting User Requests in the Context of Natural Language Standing Instructions
Nikita Moghe, Patrick Xia, Jacob Andreas +3
Users of natural language interfaces, generally powered by Large Language Models (LLMs),often must repeat their preferences each time they make a similar request. We describe an ap…
PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation
Sedrick Scott Keh, Kevin Lu, Varun Gangal +4
A personification is a figure of speech that endows inanimate entities with properties and actions typically seen as requiring animacy. In this paper, we explore the task of person…