12 citations · 18 across the 11 of their papers we have counts for
11 papers
No Single Best Model for Diversity: Learning a Router for Sample Diversity
Yuhan Liu, Fangyuan Xu, Vishakh Padmakumar +2
When posed with prompts that permit a large number of valid answers, comprehensively generating them is the first step towards satisfying a wide range of users. In this paper, we s…
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…
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…
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…
RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation
Fangyuan Xu, Weijia Shi, Eunsol Choi
Retrieving documents and prepending them in-context at inference time improves performance of language model (LMs) on a wide range of tasks. However, these documents, often spannin…
Understanding Retrieval Augmentation for Long-Form Question Answering
Hung-Ting Chen, Fangyuan Xu, Shane Arora +1
How retrieved documents are used in language models (LMs) for long-form generation task is understudied. We present two controlled studies on retrieval-augmented LM for long-form q…
Concise Answers to Complex Questions: Summarization of Long-form Answers
Abhilash Potluri, Fangyuan Xu, Eunsol Choi
Long-form question answering systems provide rich information by presenting paragraph-level answers, often containing optional background or auxiliary information. While such compr…
A Critical Evaluation of Evaluations for Long-form Question Answering
Fangyuan Xu, Yixiao Song, Mohit Iyyer +1
Long-form question answering (LFQA) enables answering a wide range of questions, but its flexibility poses enormous challenges for evaluation. We perform the first targeted study o…
Modeling Exemplification in Long-form Question Answering via Retrieval
Shufan Wang, Fangyuan Xu, Laure Thompson +2
Exemplification is a process by which writers explain or clarify a concept by providing an example. While common in all forms of writing, exemplification is particularly useful in…
How Do We Answer Complex Questions: Discourse Structure of Long-form Answers
Fangyuan Xu, Junyi Jessy Li, Eunsol Choi
Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. To better understand this complex and understudied…