Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Nan, Linyong"'
Autor:
Zhao, Yilun, Long, Yitao, Liu, Hongjun, Kamoi, Ryo, Nan, Linyong, Chen, Lyuhao, Liu, Yixin, Tang, Xiangru, Zhang, Rui, Cohan, Arman
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still largely unexpl
Externí odkaz:
http://arxiv.org/abs/2311.09805
On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
This study introduces a new long-form database question answering dataset designed to evaluate how Large Language Models (LLMs) interact with a SQL interpreter. The task necessitates LLMs to strategically generate multiple SQL queries to retrieve suf
Externí odkaz:
http://arxiv.org/abs/2311.09721
Autor:
Zhao, Yilun, Zhao, Chen, Nan, Linyong, Qi, Zhenting, Zhang, Wenlin, Tang, Xiangru, Mi, Boyu, Radev, Dragomir
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e.g., replacing key question entities or shuf
Externí odkaz:
http://arxiv.org/abs/2306.14321
Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The advancements in large language models (LLMs) have shown enormous potenti
Externí odkaz:
http://arxiv.org/abs/2305.14987
Autor:
Zhao, Yilun, Qi, Zhenting, Nan, Linyong, Mi, Boyu, Liu, Yixin, Zou, Weijin, Han, Simeng, Chen, Ruizhe, Tang, Xiangru, Xu, Yumo, Radev, Dragomir, Cohan, Arman
People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users' information needs can facilitate more efficient access to relevant data insigh
Externí odkaz:
http://arxiv.org/abs/2305.14303
Autor:
Nan, Linyong, Zhao, Yilun, Zou, Weijin, Ri, Narutatsu, Tae, Jaesung, Zhang, Ellen, Cohan, Arman, Radev, Dragomir
In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instruct
Externí odkaz:
http://arxiv.org/abs/2305.12586
Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how to generate sentences that are factually correct given the table cont
Externí odkaz:
http://arxiv.org/abs/2302.02962
Autor:
Liu, Yixin, Fabbri, Alexander R., Liu, Pengfei, Zhao, Yilun, Nan, Linyong, Han, Ruilin, Han, Simeng, Joty, Shafiq, Wu, Chien-Sheng, Xiong, Caiming, Radev, Dragomir
Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation studies for summarization either exhibit a low inter-annotator agreement or have insufficient s
Externí odkaz:
http://arxiv.org/abs/2212.07981
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they stil
Externí odkaz:
http://arxiv.org/abs/2210.12374
Autor:
Han, Simeng, Schoelkopf, Hailey, Zhao, Yilun, Qi, Zhenting, Riddell, Martin, Zhou, Wenfei, Coady, James, Peng, David, Qiao, Yujie, Benson, Luke, Sun, Lucy, Wardle-Solano, Alex, Szabo, Hannah, Zubova, Ekaterina, Burtell, Matthew, Fan, Jonathan, Liu, Yixin, Wong, Brian, Sailor, Malcolm, Ni, Ansong, Nan, Linyong, Kasai, Jungo, Yu, Tao, Zhang, Rui, Fabbri, Alexander R., Kryscinski, Wojciech, Yavuz, Semih, Liu, Ye, Lin, Xi Victoria, Joty, Shafiq, Zhou, Yingbo, Xiong, Caiming, Ying, Rex, Cohan, Arman, Radev, Dragomir
Large language models (LLMs) have achieved remarkable performance on a variety of natural language understanding tasks. However, existing benchmarks are inadequate in measuring the complex logical reasoning capabilities of a model. We present FOLIO,
Externí odkaz:
http://arxiv.org/abs/2209.00840