no code implementations • 27 Feb 2024 • Zhenting Qi, HANLIN ZHANG, Eric Xing, Sham Kakade, Himabindu Lakkaraju
Retrieval-Augmented Generation (RAG) improves pre-trained models by incorporating external knowledge at test time to enable customized adaptation.
no code implementations • 9 Dec 2023 • Zhenting Qi, Xiaoyu Tan, Shaojie Shi, Chao Qu, Yinghui Xu, Yuan Qi
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks.
1 code implementation • 25 Jun 2023 • Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev
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 shuffling table columns.
2 code implementations • 23 May 2023 • Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan
Motivated by this, we define a new query-focused table summarization task, where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored summary.
1 code implementation • 6 Feb 2023 • Yilun Zhao, Zhenting Qi, Linyong Nan, Lorenzo Jaime Yu Flores, Dragomir Radev
Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables.
1 code implementation • 22 Oct 2022 • Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, Dragomir Radev
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills.
Ranked #3 on Semantic Parsing on WikiSQL (Denotation accuracy (test) metric)
1 code implementation • 23 Sep 2022 • Zhenting Qi, Ruike Zhu, Zheyu Fu, Wenhao Chai, Volodymyr Kindratenko
In this paper, we propose a simple but effective method that solves the task from a new perspective: we design the fight detection model as a composition of an action-aware feature extractor and an anomaly score generator.
1 code implementation • 2 Sep 2022 • Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, David Peng, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Shafiq Joty, Alexander R. Fabbri, Wojciech Kryscinski, Xi Victoria Lin, Caiming Xiong, Dragomir Radev
We present FOLIO, a human-annotated, open-domain, and logically complex and diverse dataset for reasoning in natural language (NL), equipped with first order logic (FOL) annotations.