Papers by Xiangru Tang
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022.acl-demo)
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Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, Alexander Rush
| Challenge: | PromptSource is a system for creating, sharing, and using natural language prompts . prompts are used to train and query language models in zero-shot learning settings . |
| Approach: | PromptSource is a system for creating, sharing, and using natural language prompts . et al.: using prompts to train and query language models is emerging area in NLP . they propose a templating language for defining data-linked prompts, a user interface that iterates on prompt development . |
| Outcome: | PromptSource is a system for creating, sharing, and using natural language prompts . it has a templating language for defining data-linked prompts and a community-driven set of guidelines . |
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (2024.findings-acl)
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Xiangru Tang, Anni Zou, Zhuosheng Zhang, Ziming Li, Yilun Zhao, Xingyao Zhang, Arman Cohan, Mark Gerstein
| Challenge: | Large language models face unique challenges such as domain-specific terminologies and reasoning over specialized knowledge. |
| Approach: | They propose a multi-disciplinary collaboration framework that leverages LLM-based agents in a role-playing setting. |
| Outcome: | The proposed framework excels at mining and harnessing medical expertise within LLMs, as well as extending its reasoning abilities. |
FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents (2024.emnlp-main)
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Yilun Zhao, Yitao Long, Tintin Jiang, Chengye Wang, Weiyuan Chen, Hongjun Liu, Xiangru Tang, Yiming Zhang, Chen Zhao, Arman Cohan
| Challenge: | FinDVer is a benchmark to evaluate the explainable claim verification capabilities of LLMs . financial documents are typically long, intricate and dense, and they include both quantita and numerical reasoning. |
| Approach: | They propose a benchmark to evaluate the explainable claim verification capabilities of LLMs . they assess 25 LLM systems under long-context and RAG settings . |
| Outcome: | The proposed benchmark can be used to evaluate the explainable claim verification capabilities of LLMs in financial documents. |
Self-Supervised Prompt Optimization (2025.findings-emnlp)
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Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Xinbing Liang, Fengwei Teng, Jinhao Tu, Fashen Ren, Xiangru Tang, Sirui Hong, Chenglin Wu, Yuyu Luo
| Challenge: | Existing prompt optimization methods rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain. |
| Approach: | They propose a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without external reference. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt optimization methods with significantly lower costs and fewer samples. |
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. |
MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications (2024.emnlp-demo)
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Xiangru Tang, Chunyuan Deng, Hanminwang Hanminwang, Haoran Wang, Yilun Zhao, Wenqi Shi, Yi Fung, Wangchunshu Zhou, Jiannan Cao, Heng Ji, Arman Cohan, Mark Gerstein
| Challenge: | Large language models (LLMs) have evolved into interactive agents capable of planning, tool use, and task execution across various tasks. |
| Approach: | They propose a platform that leverages large language models to generate agent-tuning data for fine-tuneing smaller, specialized models. |
| Outcome: | MIMIR enables large models to simulate various roles and create interaction data, which can then be used to fine-tune open-source models like LLaMA2. |
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. |
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. |
Crosslingual Generalization through Multitask Finetuning (2023.acl-long)
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Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel
| Challenge: | Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. |
| Approach: | They apply multitask prompted finetuning to pretrained multilingual models and generate variants called BLOOMZ and mT0. |
| Outcome: | The proposed models can generalize to non-English languages that have never been seen before. |
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. |
Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries (2022.naacl-main)
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Xiangru Tang, Alexander Fabbri, Haoran Li, Ziming Mao, Griffin Adams, Borui Wang, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
| Challenge: | Existing pre-trained summarization models produce text that is factually inconsistent with the input. |
| Approach: | They present a scale-based scale for Likert rating and a scoring algorithm for Best-Worst Scaling to improve crowdsourcing reliability. |
| Outcome: | The proposed model is more reliable than existing models on two news summarization datasets. |
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning (2022.naacl-main)
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Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
| Challenge: | Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. |
| Approach: | They propose a typology of factual errors to better understand hallucinations generated by current models and a contrastive fine-tuning strategy to improve the factual consistency and overall quality of summaries. |
| Outcome: | The proposed model significantly reduces all kinds of factual errors on both SAMSum dialogue summarization and AMI meeting summarizing datasets. |
Struc-Bench: Are Large Language Models Good at Generating Complex Structured Tabular Data? (2024.naacl-short)
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| Challenge: | Large Language Models (LLMs) have advanced capabilities but produce complex structured data. |
| Approach: | They propose a structure-aware fine-tuning method to bolster LLMs' performance by crafting format-specific instructions from the intended outputs. |
| Outcome: | The proposed method outperforms LLMs on all three formats and spans text tables, HTML, and LaTeX formats. |
Unveiling the Spectrum of Data Contamination in Language Model: A Survey from Detection to Remediation (2024.findings-acl)
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| Challenge: | Data contamination is a problem in Large language models due to the reliance on extensive internet-derived training corpora. |
| Approach: | They present a survey on the topic of data contamination in large language models. |
| Outcome: | The results of the first survey on data contamination in large language models provide a comprehensive guide for NLP researchers seeking a systematic understanding of the issue. |
Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards (2025.emnlp-main)
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Jaehoon Yun, Jiwoong Sohn, Jungwoo Park, Hyunjae Kim, Xiangru Tang, Daniel Shao, Yong Hoe Koo, Ko Minhyeok, Qingyu Chen, Mark Gerstein, Michael Moor, Jaewoo Kang
| Challenge: | Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct reasoning errors at specific steps of the reasoning process. |
| Approach: | They propose a process reward modeling framework that leverages retrieval-augmented generation to verify each reasoning step against established medical knowledge bases. |
| Outcome: | The proposed model improves on five medical QA benchmarks and two open-ended diagnostic tasks by 13.50% on MedQA. |
PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes (2024.findings-emnlp)
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| Challenge: | Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific domains. |
| Approach: | They propose a framework that bridges the molecule-text modality gap by integrating a comprehensive benchmark of pretraining strategies and dataset configurations. |
| Outcome: | The proposed framework improves multimodal LLMs through cross-modal alignment and multi-graph understanding. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
Investigating Data Contamination in Modern Benchmarks for Large Language Models (2024.naacl-long)
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| Challenge: | Existing evaluation benchmarks for large language models are inflated and inconsistent with actual performance. |
| Approach: | They propose a retrieval-based system to explore potential overlaps between benchmarks and pretraining corpora and a protocol to investigate testset slot guessing. |
| Outcome: | The proposed method exploits overlaps between evaluation benchmarks and pretraining corpora and masks a wrong answer in a multiple choice question and prompts the model to fill in the gap. |
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. |
Improving Context Fidelity via Native Retrieval-Augmented Reasoning (2025.emnlp-main)
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Suyuchen Wang, Jinlin Wang, Xinyu Wang, Shiqi Li, Xiangru Tang, Sirui Hong, Xiao-Wen Chang, Chenglin Wu, Bang Liu
| Challenge: | Existing approaches to fidelity to contexts rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without improving utilization of the given context. |
| Approach: | They propose a native retrieval-augmented reasoning framework that integrates in-context evidence with the model’s own retrieval capabilities. |
| Outcome: | The proposed approach outperforms supervised fine-tuning, retrieval-augmented generation methods, and external retrieval solutions on multiple real-world and counterfactual QA benchmarks. |
Surfer100: Generating Surveys From Web Resources, Wikipedia-style (2022.lrec-1)
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Irene Li, Alex Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang, Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, Dragomir Radev
| Challenge: | Recent work on Wikipedia page generation focuses on generating the initial leading paragraph of a page, while recent pretrained language models improve upon both extractive and abstractive steps of previous models. |
| Approach: | They propose a pretrained language model that can be combined to generate Wikipedia-style summaries with sections using 100 reference human-collected surveys. |
| Outcome: | The proposed approach is compared with existing methods with 100 human-collected surveys. |