1 citations · 1 across the 8 of their papers we have counts for
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Are Finer Citations Always Better? Rethinking Granularity for Attributed Generation
Hexuan Wang, Jingyu Zhang, Benjamin Van Durme +1
Citation granularity - whether to cite individual sentences, paragraphs, or documents - is a critical design choice in attributed generation. While fine-grained citations are often…
Principled Context Engineering for RAG: Statistical Guarantees via Conformal Prediction
Debashish Chakraborty, Eugene Yang, Daniel Khashabi +2
Retrieval-Augmented Generation (RAG) enhances factual grounding in large language models (LLMs) by incorporating retrieved evidence, but LLM accuracy declines when long or noisy co…
GOLD PANNING: Strategic Context Shuffling for Needle-in-Haystack Reasoning
Adam Byerly, Daniel Khashabi
Large language models (LLMs) exhibit pronounced position bias in long-context needle-in-haystack problems, systematically prioritizing the location of information over its relevanc…
The Alignment Waltz: Jointly Training Agents to Collaborate for Safety
Jingyu Zhang, Haozhu Wang, Eric Michael Smith +7
Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing challenges: vulnerability to adv…
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…