Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Vig, Jesse"'
Large Language Models (LLMs) often exhibit positional bias in long-context settings, under-attending to information in the middle of inputs. We investigate the presence of this bias in long-form summarization, its impact on faithfulness, and various
Externí odkaz:
http://arxiv.org/abs/2410.23609
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing. However, standard chat-based conversational interfaces do not support transparency and verifiability of t
Externí odkaz:
http://arxiv.org/abs/2309.15337
Autor:
Nijkamp, Erik, Xie, Tian, Hayashi, Hiroaki, Pang, Bo, Xia, Congying, Xing, Chen, Vig, Jesse, Yavuz, Semih, Laban, Philippe, Krause, Ben, Purushwalkam, Senthil, Niu, Tong, Kryściński, Wojciech, Murakhovs'ka, Lidiya, Choubey, Prafulla Kumar, Fabbri, Alex, Liu, Ye, Meng, Rui, Tu, Lifu, Bhat, Meghana, Wu, Chien-Sheng, Savarese, Silvio, Zhou, Yingbo, Joty, Shafiq, Xiong, Caiming
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific prog
Externí odkaz:
http://arxiv.org/abs/2309.03450
Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks. However, it remains unclear whether models truly understand task definitions and whether the human-written definitions ar
Externí odkaz:
http://arxiv.org/abs/2306.01150
Autor:
Laban, Philippe, Vig, Jesse, Kryscinski, Wojciech, Joty, Shafiq, Xiong, Caiming, Wu, Chien-Sheng
Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering content - may require document-level context. Prior work has also predom
Externí odkaz:
http://arxiv.org/abs/2305.19204
State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity er
Externí odkaz:
http://arxiv.org/abs/2211.06196
Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML). However, the documentation intended to convey the model's details and appropriate us
Externí odkaz:
http://arxiv.org/abs/2205.02894
Error analysis in NLP models is essential to successful model development and deployment. One common approach for diagnosing errors is to identify subpopulations in the dataset where the model produces the most errors. However, existing approaches ty
Externí odkaz:
http://arxiv.org/abs/2203.04408
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. While recently released datasets, such as QMSum or AQuaMuSe, facilitate research efforts in Q
Externí odkaz:
http://arxiv.org/abs/2112.07637
Autor:
Choubey, Prafulla Kumar, Fabbri, Alexander R., Vig, Jesse, Wu, Chien-Sheng, Liu, Wenhao, Rajani, Nazneen Fatema
Hallucination is a known issue for neural abstractive summarization models. Recent work suggests that the degree of hallucination may depend on errors in the training data. In this work, we propose a new method called Contrastive Parameter Ensembling
Externí odkaz:
http://arxiv.org/abs/2110.07166