10 papers
Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG
Dayeon Ki, Marine Carpuat, Paul McNamee +4
Multilingual Retrieval-Augmented Generation (mRAG) systems enable language models to answer knowledge-intensive queries with citation-supported responses across languages. Despite…
FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG
Maxime Dassen, Rebecca Kotula, Kenton Murray +5
Retrieval-Augmented Generation (RAG) models are critically undermined by citation hallucinations, a deceptive failure where a model cites a source that fails to support its claim.…
DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging
Neha Verma, Kenton Murray, Kevin Duh
Structured pruning methods designed for Large Language Models (LLMs) generally focus on identifying and removing the least important components to optimize model size. However, in…
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…
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…
Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation
Dayeon Ki, Kevin Duh, Marine Carpuat
As people increasingly use AI systems in work and daily life, feedback mechanisms that help them use AI responsibly are urgently needed, particularly in settings where users are no…