Papers by Ming Tan
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for few-shot Named Entity Recognition ignore entity boundaries and are time-consuming . a seminal span-based prototypical network solves the problem using two stages: span extraction and mention classification. |
| Approach: | They propose a seminal span-based prototypical network that tackles few-shot NER . they transform sequential tags into a global boundary matrix and use prototypical learning . |
| Outcome: | The proposed model outperforms strong baselines over multiple benchmarks. |
LECO: Improving Early Exiting via Learned Exits and Comparison-based Exiting Mechanism (2023.acl-srw)
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| Challenge: | Recent work on dynamic early exiting has neglected the intermediate exits’ architectural designs. |
| Approach: | They propose a framework for learning exits and COmparison-based early exiting to improve PTMs’ early exit performance. |
| Outcome: | The proposed framework achieves the SOTA performance on multi-exit BERT training and dynamic early exiting on pre-trained models. |
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2 (2021.acl-srw)
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| Challenge: | Experimental results show that pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
| Approach: | They conduct experiments on an English essay dataset using Chinese-GPT2 . they find that the model can generate better continuations by learning to generate the in the fine-tuning stage. |
| Outcome: | The pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)
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| Challenge: | Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. |
| Approach: | They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once. |
| Outcome: | The proposed approach can perform state-of-the-art on the benchmark ACE 2005. |
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)
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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Existing infrastructure for efficient agentic data processing and model training remains underdeveloped. |
| Approach: | They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 . |
| Outcome: | The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks. |
Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)
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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang
| Challenge: | Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. |
| Approach: | They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data. |
| Outcome: | The proposed framework transforms user-generated content into user queries and generates responses from the policy model. |
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning (2024.findings-naacl)
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| Challenge: | Existing studies have explored multiple aspects that affect the performance of large language models (LLMs) such as input-output mapping, extensive data resources, and the ability to train on labeled examples. |
| Approach: | They propose a framework that injects knowledge into LLMs during continual self-supervised pre-training and judiciously selects examples with high knowledge relevance. |
| Outcome: | The proposed framework outperforms baseline models and improves by more than 13% and 7% on text classification and question-answering tasks. |
PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data (2025.findings-acl)
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Juntao Tan, Liangwei Yang, Zuxin Liu, Zhiwei Liu, Rithesh R N, Tulika Manoj Awalgaonkar, Jianguo Zhang, Weiran Yao, Ming Zhu, Shirley Kokane, Silvio Savarese, Huan Wang, Caiming Xiong, Shelby Heinecke
| Challenge: | Existing research lacks direct access to such data, making benchmarking difficult due to privacy concerns. |
| Approach: | They propose a synthetic data pipeline that generates realistic user profiles and private documents and a benchmark to evaluate models' ability to understand personal information. |
| Outcome: | The proposed pipeline generates realistic user profiles and private documents, enabling PersonaBench, a benchmark for evaluating models’ ability to understand personal information. |
Out-of-Domain Detection for Low-Resource Text Classification Tasks (D19-1)
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| Challenge: | Existing methods for OOD detection and ID classification tasks require massive amounts of ID labeled data and no OOD labeles. |
| Approach: | They propose to use OOD-resistant Prototypical Network to detect OOD cases with limited in-domain (ID) training data to solve this task. |
| Outcome: | The proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task while maintaining a competitive performance on ID classification task. |
Towards Unified Prompt Tuning for Few-shot Text Classification (2022.findings-emnlp)
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Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao
| Challenge: | Prompt-based fine-tuning has boosted performance of Pre-trained Language Models (PLMs) on few-shot text classification, but PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few- shot learning performance on downstream tasks. |
| Approach: | They propose a framework for prompt-based fine-tuning that captures prompting semantics from non-target NLP datasets and propose 'Prompt-Options-Verbalizer' for joint prompt learning across different NLP tasks. |
| Outcome: | Experiments show that the proposed framework outperforms state-of-the-art prompt-based fine-tuning frameworks on few-shot text classification tasks. |
Don’t Change Me! User-Controllable Selective Paraphrase Generation (2021.eacl-main)
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| Challenge: | a new technique allows paraphrase generation to be user-controlled . a user looking for cheap hotels in New York would not find the other answer helpful . |
| Approach: | They propose a method that provides a user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" they propose allowing user-controllable paraphrase generation by fine-tuning model that exhibits this behavior . |
| Outcome: | The proposed technique is language agnostic and tested in English and Chinese. |
NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)
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| Challenge: | Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding. |
| Approach: | They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark NER datasets and two of our proprietary NER tasks. |
LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)
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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting. |
| Approach: | LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data . |
| Outcome: | LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm . |
Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation (2026.findings-eacl)
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| Challenge: | Existing large language models (LLMs) fail to identify information gaps across diverse symptoms. |
| Approach: | They propose a Knowledge Graph-augmented LLM with active in-context learning to generate relevant and important follow-up questions. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by 5% - 8% on relevant benchmarks. |
Exploring Continual Learning for Code Generation Models (2023.acl-short)
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Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
| Challenge: | Large-scale code generation models such as Copilot and CodeT5 are expensive to train and re-train. |
| Approach: | They propose a benchmark for Continual Learning (CL) that covers a wide range of tasks with different input and output programming languages. |
| Outcome: | The proposed method improves on Prompt Pooling with Teacher Forcing, which suffers catastrophic forgetting due to stark distribution shifts in coding tasks. |
Multitask Pretraining with Structured Knowledge for Text-to-SQL Generation (2023.acl-long)
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Robert Giaquinto, Dejiao Zhang, Benjamin Kleiner, Yang Li, Ming Tan, Parminder Bhatia, Ramesh Nallapati, Xiaofei Ma
| Challenge: | Existing methods for learning representations of structured knowledge are limited to the minority of people with technical skills. |
| Approach: | They propose a large pretraining dataset and strategy for learning representations of text, tables, and SQL code that leverages the entire context of the problem. |
| Outcome: | The proposed model improves on two SQL tasks and shows a 1.7 and 2.2 percentage point improvement over existing methods. |
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence (2021.findings-emnlp)
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| Challenge: | Existing models for text segmentation use supervised and unsupervised learning to perform tasks such as text summarization and keyword extraction. |
| Approach: | They propose a transformer over transformer framework to perform neural text segmentation. |
| Outcome: | The proposed framework outperforms state-of-the-art models in terms of semantic coherence measure . bottom-level sentence encoders pre-trained on specific languages yield better performance . |
Context-Aware Conversation Thread Detection in Multi-Party Chat (D19-1)
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| Challenge: | In multi-party chat, it is common for multiple conversations to occur concurrently . a new model that automatically disentangles conversation threads is proposed . |
| Approach: | They propose a Context-Aware Thread Detection model that automatically disentangles conversation threads in chat logs. |
| Outcome: | The proposed model outperforms state-of-the-art models on four real-world chat logs. |
Learned Adapters Are Better Than Manually Designed Adapters (2023.findings-acl)
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| Challenge: | Existing approaches to improve adapter-based tuning are sub-optimal . a learning framework is proposed to learn the optimal adapter architectures . |
| Approach: | They propose a framework to automatically learn optimal adapter architectures for better task adaptation of pre-trained models. |
| Outcome: | The proposed framework outperforms the previous parameter-efficient tuning baselines while tuning comparable or fewer parameters. |
SPT: Learning to Selectively Insert Prompts for Better Prompt Tuning (2023.emnlp-main)
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| Challenge: | Prompt tuning is a method of pre-trained models that optimizes the prompt to adapt to downstream tasks. |
| Approach: | They propose a framework that learns to select the proper prompt layers by inserting a probabilistic gate at each intermediate layer. |
| Outcome: | The proposed framework can perform better than the state-of-the-art prompt tuning frameworks on ten benchmark datasets. |
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (2022.acl-short)
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Zheng Li, Zijian Wang, Ming Tan, Ramesh Nallapati, Parminder Bhatia, Andrew Arnold, Bing Xiang, Dan Roth
| Challenge: | Empirical analyses show that pre-trained sequence-to-sequence models can achieve a 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts. |
| Approach: | They propose to distill and quantize pre-trained sequence-to-sequence models to reduce memory and latency requirements. |
| Outcome: | Empirical results show that the proposed model achieves 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts on multiple summarization and QA datasets. |
PARA: Parameter-Efficient Fine-tuning with Prompt-Aware Representation Adjustment (2024.emnlp-industry)
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| Challenge: | Existing methods for parameter-efficient fine-tuning excel in the context of single-backbone multi-tenant applications. |
| Approach: | They propose to integrate a lightweight vector generator within each Transformer layer to improve prompt-aware representation adjustment. |
| Outcome: | The proposed method surpasses current benchmarks in terms of performance despite having a similar number of adjustable parameters. |
End-to-End Open-Domain Question Answering with BERTserini (N19-4)
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| Challenge: | a new open-domain question answering system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles. |
| Approach: | They propose an end-to-end question answering system that integrates BERT with an IR reader. |
| Outcome: | The proposed system improves on a standard benchmark test collection. |
ContraCLM: Contrastive Learning For Causal Language Model (2023.acl-long)
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Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang
| Challenge: | Existing studies show that causal language models lack expressiveness due to poor discrimination ability. |
| Approach: | They propose a contrastive learning framework that enhances discrimination of representations and bridges the gap with encoder-only models. |
| Outcome: | The proposed framework improves discrimination and source code generation capabilities on a variety of downstream tasks. |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features (N19-2)
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Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, Jimmy Lin
| Challenge: | e-commerce companies often have the option of escalating complaints by filing grievances with a government authority . this is detrimental to an ecommerce company, but this problem is challenging to solve by integrating recurrent neural networks with manually-engineered features. |
| Approach: | They propose a model that integrates recurrent neural networks with manually-engineered features to identify cases where the customer expresses such an intent. |
| Outcome: | The proposed model outperforms baseline models and provides better recall and triage for specialized agents. |