64 citations · 375 across the 45 of their papers we have counts for
12 papers · 2 filters
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…
Development and Evaluation of Three Chatbots for Postpartum Mood and Anxiety Disorders
Xuewen Yao, Miriam Mikhelson, S. Craig Watkins +3
In collaboration with Postpartum Support International (PSI), a non-profit organization dedicated to supporting caregivers with postpartum mood and anxiety disorders, we developed…
When to Use Efficient Self Attention? Profiling Text, Speech and Image Transformer Variants
Anuj Diwan, Eunsol Choi, David Harwath
We present the first unified study of the efficiency of self-attention-based Transformer variants spanning text, speech and vision. We identify input length thresholds (tipping poi…
Propagating Knowledge Updates to LMs Through Distillation
Shankar Padmanabhan, Yasumasa Onoe, Michael J. Q. Zhang +2
Modern language models have the capacity to store and use immense amounts of knowledge about real-world entities, but it remains unclear how to update such knowledge stored in mode…
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…
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge
Yasumasa Onoe, Michael J. Q. Zhang, Shankar Padmanabhan +2
Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes. Prior work ha…
Textless Speech-to-Speech Translation With Limited Parallel Data
Anuj Diwan, Anirudh Srinivasan, David Harwath +1
Existing speech-to-speech translation (S2ST) models fall into two camps: they either leverage text as an intermediate step or require hundreds of hours of parallel speech data. Bot…
Mitigating Temporal Misalignment by Discarding Outdated Facts
Michael J. Q. Zhang, Eunsol Choi
While large language models are able to retain vast amounts of world knowledge seen during pretraining, such knowledge is prone to going out of date and is nontrivial to update. Fu…
Continually Improving Extractive QA via Human Feedback
Ge Gao, Hung-Ting Chen, Yoav Artzi +1
We study continually improving an extractive question answering (QA) system via human user feedback. We design and deploy an iterative approach, where information-seeking users ask…
Complex Claim Verification with Evidence Retrieved in the Wild
Jifan Chen, Grace Kim, Aniruddh Sriram +2
Evidence retrieval is a core part of automatic fact-checking. Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: either no access to evide…
DIFFQG: Generating Questions to Summarize Factual Changes
Jeremy R. Cole, Palak Jain, Julian Martin Eisenschlos +3
Identifying the difference between two versions of the same article is useful to update knowledge bases and to understand how articles evolve. Paired texts occur naturally in diver…