4 citations · 7 across the 14 of their papers we have counts for
14 papers
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.…
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…
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…
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…
HLTCOE at LiveRAG: GPT-Researcher using ColBERT retrieval
Kevin Duh, Eugene Yang, Orion Weller +2
The HLTCOE LiveRAG submission utilized the GPT-researcher framework for researching the context of the question, filtering the returned results, and generating the final answer. Th…
An Interdisciplinary Approach to Human-Centered Machine Translation
Marine Carpuat, Omri Asscher, Kalika Bali +17
Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between sy…
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…
AskQE: Question Answering as Automatic Evaluation for Machine Translation
Dayeon Ki, Kevin Duh, Marine Carpuat
How can a monolingual English speaker determine whether an automatic translation in French is good enough to be shared? Existing MT error detection and quality estimation (QE) tech…
Merging Feed-Forward Sublayers for Compressed Transformers
Neha Verma, Kenton Murray, Kevin Duh
With the rise and ubiquity of larger deep learning models, the need for high-quality compression techniques is growing in order to deploy these models widely. The sheer parameter c…
Efficiency-Effectiveness Tradeoff of Probabilistic Structured Queries for Cross-Language Information Retrieval
Eugene Yang, Suraj Nair, Dawn Lawrie +3
Probabilistic Structured Queries (PSQ) is a cross-language information retrieval (CLIR) method that uses translation probabilities statistically derived from aligned corpora. PSQ i…
Where does In-context Translation Happen in Large Language Models
Suzanna Sia, David Mueller, Kevin Duh
Self-supervised large language models have demonstrated the ability to perform Machine Translation (MT) via in-context learning, but little is known about where the model performs…
Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context
Elijah Rippeth, Marine Carpuat, Kevin Duh +1
Lexical ambiguity is a challenging and pervasive problem in machine translation (\mt). We introduce a simple and scalable approach to resolve translation ambiguity by incorporating…
Anti-LM Decoding for Zero-shot In-context Machine Translation
Suzanna Sia, Alexandra DeLucia, Kevin Duh
Zero-shot In-context learning is the phenomenon where models can perform the task simply given the instructions. However, pre-trained large language models are known to be poorly c…