1 citations · 1 across the 8 of their papers we have counts for
15 papers
Query Decomposition for RAG: Balancing Exploration-Exploitation
Roxana Petcu, Kenton Murray, Daniel Khashabi +4
Retrieval-augmented generation (RAG) systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggrega…
Challenging the Evaluator: LLM Sycophancy Under User Rebuttal
Sungwon Kim, Daniel Khashabi
Large Language Models (LLMs) often exhibit sycophancy, distorting responses to align with user beliefs, notably by readily agreeing with user counterarguments. Paradoxically, LLMs…
Jointly Reinforcing Diversity and Quality in Language Model Generations
Tianjian Li, Yiming Zhang, Ping Yu +5
Post-training of Large Language Models (LMs) often prioritizes accuracy and helpfulness at the expense of diversity. This creates a tension: while post-training improves response q…
Evaluating the Evaluators: Are readability metrics good measures of readability?
Isabel Cachola, Daniel Khashabi, Mark Dredze
Plain Language Summarization (PLS) aims to distill complex documents into accessible summaries for non-expert audiences. In this paper, we conduct a thorough survey of PLS literatu…
Hell or High Water: Evaluating Agentic Recovery from External Failures
Andrew Wang, Sophia Hager, Adi Asija +2
As language model agents are applied to real world problems of increasing complexity, they will be expected to formulate plans across large search spaces. If those plans fail for r…
The Translation Barrier Hypothesis: Multilingual Generation with Large Language Models Suffers from Implicit Translation Failure
Niyati Bafna, Tianjian Li, Kenton Murray +4
Multilingual generation with large language models (LLMs) is often of poor quality for mid- to low-resource languages, but the causes for this are not well-understood. We first dem…