1 citations · 2 across the 18 of their papers we have counts for
6 papers · 1 filter
RefreshKV: Updating Small KV Cache During Long-form Generation
Fangyuan Xu, Tanya Goyal, Eunsol Choi
Generating long sequences of tokens given a long-context input is a very compute-intensive inference scenario for large language models (LLMs). One prominent inference speed-up app…
Contrastive Learning to Improve Retrieval for Real-world Fact Checking
Aniruddh Sriram, Fangyuan Xu, Eunsol Choi +1
Recent work on fact-checking addresses a realistic setting where models incorporate evidence retrieved from the web to decide the veracity of claims. A bottleneck in this pipeline…
Open-World Evaluation for Retrieving Diverse Perspectives
Hung-Ting Chen, Eunsol Choi
We study retrieving a set of documents that covers various perspectives on a complex and contentious question (e.g., will ChatGPT do more harm than good?). We curate a Benchmark fo…
Long-Form Answers to Visual Questions from Blind and Low Vision People
Mina Huh, Fangyuan Xu, Yi-Hao Peng +5
Vision language models can now generate long-form answers to questions about images - long-form visual question answers (LFVQA). We contribute VizWiz-LF, a dataset of long-form ans…
CaLMQA: Exploring culturally specific long-form question answering across 23 languages
Shane Arora, Marzena Karpinska, Hung-Ting Chen +3
Despite rising global usage of large language models (LLMs), their ability to generate long-form answers to culturally specific questions remains unexplored in many languages. To f…
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
Fangyuan Xu, Kyle Lo, Luca Soldaini +3
Large language models (LLMs) adapted to follow user instructions are now widely deployed as conversational agents. In this work, we examine one increasingly common instruction-foll…