Papers by Linyong Nan
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
OpenRT: An Open-source Framework for Reasoning Over Tabular Data (2023.acl-demo)
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| Challenge: | Existing table pre-training methods are benchmarked on a limited number of datasets with varying configurations, resulting in a lack of unified, standardized, fair, and comprehensive comparison between methods. |
| Approach: | They propose to use OpenRT to reproduce existing table pre-training models and develop new models quickly. |
| Outcome: | The proposed framework reproduces existing table pre-training models and compares them against four question answering, one fact checking, and one faithful text generation datasets. |
OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)
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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan
| Challenge: | Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets. |
| Approach: | They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models. |
| Outcome: | The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work. |
DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)
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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Data-to-text annotations can be costly when dealing with tables with nontrivial structures. |
| Approach: | They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title. |
| Outcome: | The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables. |
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (2022.emnlp-main)
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| Challenge: | Existing models with table-specific architectures and pre-training methods perform well on understanding table structures but lack table reasoning skills. |
| Approach: | They propose to pre-train tables with table reasoning skills without complex architectures . they define 7 table reasoning skill, and then pre-teach them to generate tables . |
| Outcome: | The proposed model improves on four tasks and is available on github. |
Leveraging Locality in Abstractive Text Summarization (2022.emnlp-main)
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Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed Hassan Awadallah, Dragomir Radev
| Challenge: | Neural attention models have improved on many natural language processing tasks, but their quadratic memory complexity hinders their applications in long text summarization. |
| Approach: | They propose to use a restricted context to study locality in text summarization . they propose to employ a quadratic memory growth with respect to the input length . |
| Outcome: | The proposed model has better performance than baseline models with efficient attention modules. |
R2D2: Robust Data-to-Text with Replacement Detection (2022.emnlp-main)
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| Challenge: | Existing methods to mitigate unfaithful text generation are inadequate . data-to-text generation requires a structured input format . |
| Approach: | They propose a training framework that addresses unfaithful Data-to-Text generation by training a system as a generator and faithfulness discriminator with additional replacement detection and unlikelihood learning tasks. |
| Outcome: | The proposed training framework improves FeTaQA, LogicNLG, and ToTTo fidelity on D2T systems. |
FeTaQA: Free-form Table Question Answering (2022.tacl-1)
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Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, Dragomir Radev
| Challenge: | Existing table-based question answering datasets lack advanced information-based questions that require reasoning and integration of information pieces retrieved from structured knowledge sources. |
| Approach: | They propose a dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs that can be used to generate an answer. |
| Outcome: | The proposed dataset has 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs. |
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations (2023.acl-long)
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Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev
| Challenge: | Existing Table QA models are vulnerable to task-specific perturbations, such as replacing key question entities or shuffling table columns. |
| Approach: | They propose to use large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models. |
| Outcome: | The proposed model significantly improves on existing Table QA models against human-annotated adversarial perturbations. |
OMG-QA: Building Open-Domain Multi-Modal Generative Question Answering Systems (2024.emnlp-industry)
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| Challenge: | Existing approaches to QA require multiple modalities and a broad pool of information sources to generate coherent answers. |
| Approach: | They propose a new resource to evaluate the effectiveness of question answering systems that perform retrieval augmented generation in scenarios that demand reasoning on multi-modal, multi-document contexts. |
| Outcome: | The proposed method evaluates question answering systems that perform retrieval augmented generation (RAG) in open-domain questions . it requires systems to navigate diverse modalities and a broad pool of information sources, making it uniquely challenging. |
Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios (2023.emnlp-industry)
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| Challenge: | Existing table-to-text generation techniques that transform complex tabular data into comprehensible narratives are lacking in real-world applications. |
| Approach: | They investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios. |
| Outcome: | The proposed models can generate table-to-text data in two real-world information seeking scenarios and perform better than existing models. |
On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering (2024.findings-naacl)
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| Challenge: | a new study evaluates how Large Language Models interact with a SQL interpreter . the model is limited in context and is stochastic, making it less suited for tasks requiring high precision and extensive computations. |
| Approach: | They propose and evaluate two interaction strategies to evaluate how LLMs interact with a SQL interpreter. |
| Outcome: | The proposed framework improves the accuracy and reliability of the evaluations. |
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control (2023.eacl-main)
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| Challenge: | Existing models for LT2T generation focus on surface-level realizations without much logical inference. |
| Approach: | They propose a model that uses logic forms as fact verifiers and content planners to control LT2T generation. |
| Outcome: | Experimental results show that the proposed model addresses unfaithfulness and diversity issues simultaneously. |
Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)
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Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir Radev
| Challenge: | In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory. |
| Approach: | They propose to extend in-context learning to question answering tasks that utilize structured knowledge sources and to explore various prompt design strategies for employing LLMs. |
| Outcome: | The proposed approach outperforms the state-of-the-art system by 2.5 points and the best fine-tuned system by 5.1 points on the Spider dataset. |
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages (2020.coling-main)
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| Challenge: | We train monolingual and cross-lingual classifiers on the extracted features of tweets . we use a few state-of-the-art contextual embeddings to extract features of the tweets. |
| Approach: | They propose to use tweets to train a dataset of English and two low-resource languages to train zero-shot transfer models. |
| Outcome: | The proposed model performs well in English and in low-resource languages . the proposed model is based on state-of-the-art embeddings and semi-supervised methods . |
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation (2023.acl-long)
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Yixin Liu, Alex Fabbri, Pengfei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev
| Challenge: | Existing studies for summarization evaluation exhibit low inter-annotator agreement or lack scale. |
| Approach: | They propose a modified summarization salience protocol based on fine-grained semantic units and a robust summarizing evaluation benchmark. |
| Outcome: | The proposed protocol is based on fine-grained semantic units and allows for high inter-annotator agreement. |
QTSumm: Query-Focused Summarization over Tabular Data (2023.emnlp-main)
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Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan
| Challenge: | Existing text generation systems that can provide accurate table summaries can facilitate more efficient access to relevant data insights. |
| Approach: | They propose a query-focused task where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored table summary. |
| Outcome: | The proposed method improves existing baselines on table-to-text generation and large language models by concatenating generated facts to the model input. |