Papers by Michael Li

On the Reliability of Psychological Scales on Large Language Models (2024.emnlp-main)

Challenge:	Existing learning metrics are limited to tasks where large human ratings are available.
Approach:	They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation.
Outcome:	The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation.

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Jen-tse Huang, Wenxiang Jiao, Man Ho Lam, Eric John Li, Wenxuan Wang, Michael Lyu

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)

Challenge:	Recent research has focused on examining Large Language Models’ characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics.
Approach:	They propose to examine the reliability of personality tests to LLMs by using psychological scales.
Outcome:	The proposed model can represent diverse personalities with specific prompt instructions.

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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger

Text Classification

Challenge:	Language identification (LID) is a fundamental step in curating multilingual corpora.
Approach:	They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages.
Outcome:	The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain.

Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls (2025.emnlp-main)

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Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-Wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu

InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)

Challenge:	a large-scale empirical study compares natural web data, diverse synthetic types, and mixtures of natural and synthetic data.
Approach:	They conduct a large-scale empirical study on large-volume LLMs using a unified protocol and scaling laws.
Outcome:	The proposed method is faster than pre-training on natural web data, the authors show . their results are consistent with previous studies on rephrased text and textbooks .

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Kaixin Li, Qisheng Hu, James Zhao, Hui Chen, Yuxi Xie, Tiedong Liu, Michael Shieh, Junxian He

Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings (2020.emnlp-main)

Challenge:	InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing.
Approach:	They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing .
Outcome:	The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark .

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Yue Wang, Jing Li, Michael Lyu, Irwin King

Multimodality

Challenge:	Existing studies focus on text modeling, ignoring the rich features embedded in the matching images.
Approach:	They propose a novel multi-modal multi-head attention model to capture cross-media interactions and image wordings to bridge the two modalities.
Outcome:	The proposed model outperforms the current state of the art based on text modeling and image matching .

Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning (2020.findings-emnlp)

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Yifan Gao, Piji Li, Wei Bi, Xiaojiang Liu, Michael Lyu, Irwin King

Dialogue Systems & Conversational Agents

Challenge:	Sentence function is an important linguistic feature indicating the communicative purpose of a sentence in a conversation.
Approach:	They propose a structured meta-learning approach for dialogue generation on infrequent sentence functions.
Outcome:	The proposed approach improves informativeness and relevance of dialogue generation on infrequent sentence functions while preserving knowledge generalization for similar sentence functions.

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

Information Extraction & Text Mining

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines (2026.findings-acl)

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Jiechao Gao, Rohan Kumar Yadav, Yuangang Li, Yuandong Pan, Jie Wang, Ying Liu, Michael Lepech

Explainability & Interpretability in NLP

Challenge:	Pretrained language models (PLMs) provide strong semantic representations but are costly and opaque.
Approach:	They propose a framework that transfers pretrained language models into symbolic form and integrates them into symbolic models.
Outcome:	The proposed framework improves interpretability and accuracy across multiple text classification tasks while remaining fully symbolic and efficient.

Few-shot Natural Language Generation for Task-Oriented Dialog (2020.findings-emnlp)

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Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, Jianfeng Gao

Every Answer Matters: Evaluating Commonsense with Probabilistic Measures (2024.acl-long)

Challenge:	Existing methods for NLG depend on heavily annotated data, which is infeasible for new domains.
Approach:	They propose a system that converts a dialog act into a response in natural language . they propose 'nuclear language generation' to simulate a few-shot learning setting .
Outcome:	The proposed model outperforms existing methods on a large set of annotated datasets.

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Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Li

Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading (2020.emnlp-main)

Challenge:	Existing commonsense evaluations are often posed as multiple-choice questions, allowing models to exploit systematic biases.
Approach:	They propose a generative task that evaluates common sense via multiple open-ended generations and a method that strongly correlates with human judgments.
Outcome:	The proposed method outperforms strong language model baselines on a dataset of human and machine common sense.

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Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C.H. Hoi, Caiming Xiong, Irwin King, Michael Lyu

Natural Language Interfaces

Challenge:	Document interpretation and dialog understanding are the two major challenges for conversational machine reading.
Approach:	They propose a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of document and dialog.
Outcome:	The proposed model improves document interpretation and dialog understanding on the ShARC benchmark.

ModSCAN: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities (2024.emnlp-main)

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Yukun Jiang, Zheng Li, Xinyue Shen, Yugeng Liu, Michael Backes, Yang Zhang

Model Internal Sleuthing: Finding Lexical Identity and Inflectional Features in Modern Language Models (2026.acl-long)

Challenge:	Large vision-language models have been widely used but stereotypical biases are unexplored.
Approach:	They propose a framework to SCAN stereotypical bias within large vision-language models . they examine stereotype biases with respect to gender and race in three scenarios .
Outcome:	The proposed framework can reduce stereotypical biases in large vision-language models . the currently popular models show significant stereotype biase .

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Michael Li, Nishant Subramani

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning (2021.naacl-main)

Challenge:	Prior work suggests hierarchical organization where different layers specialize in capturing distinct levels of linguistic structure.
Approach:	They probe 25 models from BERT Base to Qwen2.5-7B focusing on linguistic properties: lexical identity and inflectional features.
Outcome:	The proposed model maintains inflectional features across layers while trading off lexical identity for compact, predictive representations.

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Xuelu Chen, Michael Boratko, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, Andrew McCallum

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

Challenge:	Existing methods to generalize knowledge bases model triple-level uncertainty . Existing models only model triple level uncertainty, and reasoning results lack global consistency.
Approach:	They propose a method to embed knowledge graphs with calibrated probabilistic semantics . they model each entity as a box and relations between two entities as affine transforms based on affinity transforms.
Outcome:	Experiments show that the proposed method outperforms baseline methods on confidence prediction and fact ranking.

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings (2022.acl-long)

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

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Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum

Representation Learning

Challenge:	Word2Box provides a set-theoretic training objective for learning word representations . word representation is not natural, all senses and contexts, levels of abstraction, variants and modifications which the word may represent are forced to be captured by mat t is nunc.
Approach:	They propose a fuzzy-set interpretation of box embeddings and learn box representations of words using a set-theoretic training objective.
Outcome:	The proposed model improves word similarity tasks on less common words.

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia (2025.acl-long)

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Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung, Frederikus Hudi, David Anugraha, Muhammad Ravi Shulthan Habibi, Muhammad Reza Qorib, Amit Agarwal, Joseph Marvin Imperial, Hitesh Laxmichand Patel, Vicky Feliren, Bahrul Ilmi Nasution, Manuel Antonio Rufino, Genta Indra Winata, Rian Adam Rajagede, Carlos Rafael Catalan, Mohamed Fazli Mohamed Imam, Priyaranjan Pattnayak, Salsabila Zahirah Pranida, Kevin Pratama, Yeshil Bangera, Adisai Na-Thalang, Patricia Nicole Monderin, Yueqi Song, Christian Simon, Lynnette Hui Xian Ng, Richardy Lobo Sapan, Taki Hasan Rafi, Bin Wang, null Supryadi, Kanyakorn Veerakanjana, Piyalitt Ittichaiwong, Matthew Theodore Roque, Karissa Vincentio, Takdanai Kreangphet, Phakphum Artkaew, Kadek Hendrawan Palgunadi, Yanzhi Yu, Rochana Prih Hastuti, William Nixon, Mithil Bangera, Adrian Xuan Wei Lim, Aye Hninn Khine, Hanif Muhammad Zhafran, Teddy Ferdinan, Audra Aurora Izzani, Ayushman Singh, Evan Evan, Jauza Akbar Krito, Michael Anugraha, Fenal Ashokbhai Ilasariya, Haochen Li, John Amadeo Daniswara, Filbert Aurelian Tjiaranata, Eryawan Presma Yulianrifat, Can Udomcharoenchaikit, Fadil Risdian Ansori, Mahardika Krisna Ihsani, Giang Nguyen, Anab Maulana Barik, Dan John Velasco, Rifo Ahmad Genadi, Saptarshi Saha, Chengwei Wei, Isaiah Edri W. Flores, Kenneth Chen Ko Han, Anjela Gail D. Santos, Wan Shen Lim, Kaung Si Phyo, Tim Santos, Meisyarah Dwiastuti, Jiayun Luo, Jan Christian Blaise Cruz, Ming Shan Hee, Ikhlasul Akmal Hanif, M.Alif Al Hakim, Muhammad Rizky Sya’ban, Kun Kerdthaisong, Lester James Validad Miranda, Fajri Koto, Tirana Noor Fatyanosa, Alham Fikri Aji, Jostin Jerico Rosal, Jun Kevin, Robert Wijaya, Onno P. Kampman, Ruochen Zhang, Börje F. Karlsson, Peerat Limkonchotiwat

Multimodality

Challenge:	Southeast Asia is underrepresented in vision-language research . SEA-VL is an open-source initiative dedicated to developing culturally relevant datasets for SEA languages.
Approach:	They propose to use crowdsourced, automated image crawling and synthetic image generation to develop culturally relevant datasets for SEA languages.
Outcome:	The proposed datasets capture SEA cultural nuances and contexts better than existing datasets.

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training (2026.findings-acl)

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Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang

ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning (2020.emnlp-main)

Challenge:	Misaligned large language models can magnify harm by exploiting them to undermine safety . et al., 2022b; Bai e.t., 2023): misalignment, realignment and model-specific resistance are important .
Approach:	They evaluate four methods to identify a mechanism asymmetry between attack and defense . they find that ORPO is most effective for misalignment, but DPO excels in realignment .
Outcome:	The proposed methods show a mechanism asymmetry between attack and defense . the proposed methods excel in realignment, but at the expense of model utility .

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Michael Boratko, Xiang Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum

Reasoning

Challenge:	Existing question answering datasets for common sense reasoning are lacking for prototypical situations.
Approach:	They propose a question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations.
Outcome:	The proposed model outperforms existing models on all evaluation metrics with a meaningful gap.

Do Transformer Modifications Transfer Across Implementations and Applications? (2021.emnlp-main)

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Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel

Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages (2025.naacl-long)

Challenge:	Currently, the Transformer is the de facto architecture of choice for processing sequential data.
Approach:	They evaluate the Transformer architecture and its modifications in a shared experimental setting . they conjecture that performance improvements may strongly depend on implementation details .
Outcome:	The proposed improvements do not significantly improve performance, the authors find . the proposed improvements are either developed in the same codebase or are minor changes .

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Max Zuo, Francisco Piedrahita Velez, Xiaochen Li, Michael Littman, Stephen Bach

Automatic Prompt Optimization with “Gradient Descent” and Beam Search (2023.emnlp-main)

Challenge:	Existing evaluation methods struggle to ensure semantic correctness and rely on simple or unrealistic datasets.
Approach:	They propose a benchmark to evaluate language models’ ability to generate PDDL code from natural language descriptions of planning tasks.
Outcome:	The proposed benchmark evaluates the ability of language models to generate PDDL code from natural language descriptions of planning tasks against ground truth and a dataset of 145,918 text-to-PDDL pairs with varying levels of difficulty.

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Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, Michael Zeng

ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents (2020.acl-demos)

Challenge:	Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort.
Approach:	They propose an algorithm that uses numerical gradient descent to automatically improve prompts by rewriting vague task descriptions into more precise annotation instructions.
Outcome:	The proposed algorithm outperforms previous methods and improves performance on three benchmark NLP tasks and the novel problem of LLM jailbreak detection.

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Chia-Yu Li, Daniel Ortega, Dirk Väth, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz Völkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic, Ngoc Thang Vu

Dialogue Systems & Conversational Agents

Challenge:	Existing toolkits for developing dialog systems are limited to core components and do not support multi-modal processing and social signals.
Approach:	They propose to use ADVISER to develop multi-modal dialog agents using multi-text and social signals.
Outcome:	The proposed toolkit is flexible, easy to use, and easy to extend for linguists and cognitive scientists, thereby providing a flexible platform for collaborative research.

Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs (2026.acl-long)

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Wai Man Si, Mingjie Li, Michael Backes, Yang Zhang

Responsible & Trustworthy NLP

Challenge:	Empirical evaluations on ML models show substantial reductions in unsafe generations and improved robustness against jailbreak attacks.
Approach:	They propose a resource-efficient pruning framework that directly identifies unsafe behaviors while preserving model utility.
Outcome:	The proposed framework reduces unsafe generations and improves robustness against jailbreak attacks with minimal utility loss.

Open Schrödinger’s Closed Box: Identifying Retrieval Augmented Generation in API-Accessible Large Language Model Services (2026.acl-long)

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Yukun Jiang, Xinyue Shen, Michael Backes, Zheng Li, Yang Zhang

Robustness in NLP

Challenge:	Large language models (LLMs) are powerful at question-answering but prone to hallucinations due to limited domain-specific or up-to-date knowledge.
Approach:	They propose a framework for IDentifying RAG properties in LLM services that integrates LLMs with retrieval systems and adds an external retriever and knowledge database to mitigate hallucinations.
Outcome:	The proposed framework detects RAG-enhanced LLMs with 99.97% accuracy with partial or no optional knowledge and nearly 100% when the LLM and database are known.

FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows (2022.emnlp-main)

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Jianqiao Zhao, Yanyang Li, Wanyu Du, Yangfeng Ji, Dong Yu, Michael Lyu, Liwei Wang

Dialogue Systems & Conversational Agents

Challenge:	Despite recent progress in dialogue evaluation, how to develop automatic metrics remains an open problem.
Approach:	They propose a consensus-based framework for dialog evaluation using segment act flows . they propose to crowdsource a large-scale dataset for it to be evaluated .
Outcome:	The proposed framework can reach the best or comparable correlation with human evaluation.

Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach (2024.emnlp-industry)

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Zhuowan Li, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky

Making Long-Context Language Models Better Multi-Hop Reasoners (2024.acl-long)

Challenge:	Recent LLMs like Gemini-1.5 and GPT-4 show exceptional capabilities to understand long contexts directly.
Approach:	They propose a method that routes queries to RAG or LC based on model self-reflection.
Outcome:	The proposed method significantly reduces the computation cost while maintaining a comparable performance to RAG.

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Yanyang Li, Shuo Liang, Michael Lyu, Liwei Wang

AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)

Challenge:	Recent advances in long-context modeling have enhanced language models for complex tasks, but they struggle with multi-hop reasoning and noisy contexts.
Approach:	They propose an approach that prompts LMs to supply attributions for each assertion during reasoning.
Outcome:	The proposed model achieves competitive performance on multi-hop reasoning benchmarks, closely paralleling proprietary LMs such as ChatGPT and Claude-instant.

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Yulong Chen, Yang Liu, Li Dong, Shuohang Wang, Chenguang Zhu, Michael Zeng, Yue Zhang

Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine (2026.findings-acl)

Challenge:	Prompt-based learning can tackle zero-shot and few-shot NLP tasks . authors propose a method that makes use of pre-trained language models .
Approach:	They propose to map NLP tasks into natural language prompts, which are then filled by pre-trained language models.
Outcome:	The proposed method outperforms standard prompt-based methods in few-shot settings.

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Sebastian Antony Joseph, Lily Chen, Barry Wei, Michael Mackert, Iain James Marshall, Paul Pu Liang, Ramez Kouzy, Byron C Wallace, Junyi Jessy Li

Reasoning

Challenge:	Evidence-based medicine connects to every individual, yet the nature of it is highly technical . e-fact-checking systems that connect to medical decisions are largely unused . we examine how clinical experts verify real claims from social media .
Approach:	They propose that fact-checking should be approached as an interactive communication problem . they argue that social media and AI have made medical knowledge accessible .
Outcome:	The proposed method is based on the work of a clinical expert on social media . it reveals that the method is difficult to connect claims to clinical trials .

Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation (2025.findings-acl)

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Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit, Peidong Wang, Jian Xue, Dinesh Manocha, Jinyu Li

Building Adaptive Acceptability Classifiers for Neural NLG (2021.emnlp-main)

Challenge:	Generative Error Correction (GEC) is a powerful post-processing method to boost the performance of Automatic Speech Recognition systems.
Approach:	They propose a method to augment GEC models with retrieved entities to improve accuracy in out-of-domain and out-od scenarios.
Outcome:	The proposed method outperforms baseline models on multiple datasets and settings.

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Soumya Batra, Shashank Jain, Peyman Heidari, Ankit Arun, Catharine Youngs, Xintong Li, Pinar Donmez, Shawn Mei, Shiunzu Kuo, Vikas Bhardwaj, Anuj Kumar, Michael White

Discovering Language Model Behaviors with Model-Written Evaluations (2023.findings-acl)

Challenge:	Existing approaches to generate synthetic data using simple sentence transformations and/or model-based techniques may not generate realistic error samples with respect to the NLG models.
Approach:	They propose a framework to train models to classify acceptability of responses generated by natural language generation models using a 2-stage approach . they use existing sentence transformations to generate samples that better resemble the output of the generation models.
Outcome:	The proposed approach outperforms existing techniques and can be used in few-shot settings using self-training.

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Ethan Perez, Sam Ringer, Kamile Lukosiute, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Benjamin Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemi Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan

SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading (2024.emnlp-main)

Challenge:	Prior work creates evaluations with crowdwork or existing data sources, which are not always available.
Approach:	They generate evaluations automatically with language models (LMs) using crowdwork or existing data sources to find out how they behave .
Outcome:	The results show that large LMs repeat back a dialog user’s preferred answer and express greater desire to pursue concerning goals like resource acquisition and goal preservation.

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Tu Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Jianfeng Gao, Fabian Peller-Konrad, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues

Bridging the Preference Gap between Retrievers and LLMs (2024.acl-long)

Challenge:	Large Language Models (LLMs) are rapidly developing and are becoming more and more useful in scientific tasks.
Approach:	They propose to use LLM-as-a-judge to grade LLMs on SciEx to assess their ability on scientific tasks.
Outcome:	The proposed benchmarks show that the LLMs perform decently on free-form exams, achieving 0.948 Pearson correlation with expert grading.

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Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky

Reward Yourself: Efficient Self Rewards for Trustworthy Sampling (2026.findings-acl)

Challenge:	Existing studies on retrievers and LLMs treat them as separate components . a novel bridge model is proposed to optimize the relationship between the retriever and the LLM .
Approach:	They propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM.
Outcome:	Empirical results show that the proposed model optimizes the connection between the retriever and the LLM.

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Mingjie Li, Wai Man Si, Michael Backes, Yang Zhang

Responsible & Trustworthy NLP

Challenge:	Retraining reward models to address privacy leaks and stereotypes is expensive . recent advances in large language models have led to improvements in understanding .
Approach:	They propose a lightweight intrinsic reward that can be used to prune existing LLMs to approximate an "untrust" and an ""untrust "" token distribution.
Outcome:	Experiments with two reward models and four LLMs show that selfRW improves trustworthiness with minimal impact on general utility benchmarks.

Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation (2022.emnlp-main)

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Yanyang Li, Jianqiao Zhao, Michael Lyu, Liwei Wang

Low-Resource NLP

Challenge:	Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text.
Approach:	They propose a posterior-based reweighing and noisy training strategy to exploit generated knowledge in dialogue generation.
Outcome:	Empirical results show that the proposed methods outperform the state-of-the-art methods in unsupervised knowledge-grounded conversation.

Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (2022.findings-naacl)

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Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, Michael Lyu

Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)

Challenge:	Existing studies on keyphrase generation on non-English languages haven’t been vastly investigated.
Approach:	They propose a retrieval-augmented method for multilingual keyphrase generation that leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages.
Outcome:	The proposed model outperforms baselines on non-English keyphrase generation datasets and the proposed model is scalable.

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Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino

DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)

Challenge:	Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations.
Approach:	They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition.
Outcome:	The proposed method achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task.

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Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li

TC-Bench: Benchmarking Temporal Compositionality in Conditional Video Generation (2025.findings-acl)

Challenge:	Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance.
Approach:	They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads.
Outcome:	The proposed model can be adapted to various attention-related models and achieves high performance.

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Weixi Feng, Jiachen Li, Michael Saxon, Tsu-Jui Fu, Wenhu Chen, William Yang Wang

CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)

Challenge:	Existing video generation models struggle to interpret compositional changes and synthesize components across different time steps.
Approach:	They propose a temporal compositionality benchmark that uses text prompts and ground truth videos to evaluate compositional changes in video.
Outcome:	The proposed benchmark can be used for text-to-video and image-to video generation.

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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang

Representations of Fact, Fiction and Forecast in Large Language Models: Epistemics and Attitudes (2025.acl-long)

Challenge:	Large language models (LLMs) have revolutionized natural language processing.
Approach:	They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy.
Outcome:	CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding.

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Meng Li, Michael Vrazitulis, David Schlangen