Xiong, Caiming

Binding Language Models in Symbolic Languages

Cheng, Z., Xie, T., Shi, P., Li, C., Nadkarni, R., Hu, Y., Xiong, C., Radev, D., Ostendorf, M., Zettlemoyer, L., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 53

DialFact: A Benchmark for Fact-Checking in Dialogue

Gupta, P., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Misinformation and Its ImpactsSpeech and dialogue systems

Cited by 48

FOLIO: Natural Language Reasoning with First-Order Logic

Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Zhou, W., Coady, J., Peng, D., Qiao, Y., Benson, L., et al. (2022) · arXiv

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 35

Beyond the Chat: Executable and Verifiable Text-Editing with LLMs

Laban, P., Vig, J., Hearst, M., Xiong, C., & Wu, C.-S. (2024) · Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology

Usability and User Interface DesignSoftware Engineering Research

Cited by 31

MixQG: Neural Question Generation with Mixed Answer Types

Murakhovs’ka, L., Wu, C.-S., Laban, P., Niu, T., Liu, W., & Xiong, C. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022

Topic ModelingNatural Language Processing Techniques

Cited by 31

Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems

Laban, P., Fabbri, A. R., Xiong, C., & Wu, C.-S. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 26

Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation

Laban, P., Wu, C.-S., Murakhovs'ka, L., Liu, W., & Xiong, C. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 16

Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?

Cao, R., Lei, F., Wu, H., Chen, J., Fu, Y., Gao, H., Xiong, X., Zhang, H., Mao, Y., Hu, W., et al. (2024) · Advances in Neural Information Processing Systems 37

Web Data Mining and AnalysisScientific Computing and Data Management

Cited by 15

Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets

Liu, Z., Hoang, T., Zhang, J., Zhu, M., Lan, T., Kokane, S., Tan, J., Yao, W., Liu, Z., Feng, Y., et al. (2024) · Advances in Neural Information Processing Systems 37

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 13

APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets

Liu, Z., Hoang, T., Zhang, J., Zhu, M., Lan, T., Kokane, S., Tan, J., Yao, W., Liu, Z., Feng, Y., et al. (2024) · arXiv

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 13

LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond

Laban, P., Kryściński, W., Agarwal, D., Fabbri, A. R., Xiong, C., Joty, S., & Wu, C.-S. (2023) · arXiv

Misinformation and Its ImpactsTopic Modeling

Cited by 13

BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents

Liu, Z., Yao, W., Zhang, J., Xue, L., Heinecke, S., Murthy, R., Feng, Y., Chen, Z., Niebles, J. C., Arpit, D., et al. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 11

Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization

Yao, W., Heinecke, S., Niebles, J. C., Liu, Z., Feng, Y., Xue, L., Murthy, R., Chen, Z., Zhang, J., Arpit, D., et al. (2023) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 10

AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System

Liu, Z., Yao, W., Zhang, J., Yang, L., Liu, Z., Tan, J., Choubey, P. K., Lan, T., Wu, J., Wang, H., et al. (2024) · arXiv

AI-based Problem Solving and PlanningNatural Language Processing Techniques

Cited by 10

Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning

Yin, F., Vig, J., Laban, P., Joty, S., Xiong, C., & Wu, C.-S. J. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 10

Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

Lei, F., Chen, J., Ye, Y., Cao, R., Shin, D., Su, H., Suo, Z., Gao, H., Hu, W., Yin, P., et al. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 7

Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis

Xie, T., Deng, J., Li, X., Yang, J., Wu, H., Chen, J., Hu, W., Wang, X., Xu, Y., Wang, Z., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 7

A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

Ke, Z., Jiao, F., Ming, Y., Nguyen, X.-P., Xu, A., Long, D. X., Li, M., Qin, C., Wang, P., Savarese, S., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Luo, Z., Shen, Z., Yang, W., Zhao, Z., Jwalapuram, P., Saha, A., Sahoo, D., Savarese, S., Xiong, C., & Li, J. (2025) · arXiv

Topic ModelingArtificial Intelligence Applications

Cited by 3

FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"

Ming, Y., Purushwalkam, S., Pandit, S., Ke, Z., Nguyen, X.-P., Xiong, C., & Joty, S. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking

Niu, T., Joty, S., Liu, Y., Xiong, C., Zhou, Y., & Yavuz, S. (2024) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 2

APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

Prabhakar, A., Liu, Z., Zhu, M., Zhang, J., Awalgaonkar, T., Wang, S., Liu, Z., Chen, H., Hoang, T., Niebles, J. C., et al. (2025) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 2

Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents

Zhang, K., Yao, W., Liu, Z., Feng, Y., Liu, Z., Murthy, R., Lan, T., Li, L., Lou, R., Xu, J., et al. (2024) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 1

Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding

Chen, H., Feng, Y., Liu, Z., Yao, W., Prabhakar, A., Heinecke, S., Ho, R., Mui, P., Savarese, S., Xiong, C., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1