Papers by Michael Chen
Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)
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Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong
| Challenge: | Existing fashion recommendation systems struggle with the unique challenges of the fashion domain. |
| Approach: | They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts. |
| Outcome: | The proposed framework significantly improves fashion recommendation performance on Amazon fashion. |
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions (2025.emnlp-main)
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Emmy Liu, Amanda Bertsch, Lintang Sutawika, Lindia Tjuatja, Patrick Fernandes, Lara Marinov, Michael Chen, Shreya Singhal, Carolin Lawrence, Aditi Raghunathan, Kiril Gashteovski, Graham Neubig
| Challenge: | Language model performance is largely dependent on pretraining decisions, but scaling laws based on only these two aspects do not always explain downstream task performance. |
| Approach: | They meta-analyze 92 open-source pretrained models to quantify their impact on performance. |
| Outcome: | The framework lays a foundation for more systematic investigation of how model development choices shape final capabilities. |
“Feels Feminine to Me”: Understanding Perceived Gendered Style through Human Annotations (2025.emnlp-main)
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| Challenge: | Using gender identity-based framing, language–gender associations are often grounded in the author’s gender identity, inferred from their language use. |
| Approach: | They propose to operationalize the language–gender association as a perceived gender expression of language, focusing on how expression is externally interpreted by humans, independent of the author’s gender identity. |
| Outcome: | The first dataset of itskind identifies 5,100 human annotations of perceived gendered style—human-written texts rated on a five-point scale from very feminine to very masculine. |
Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data (2022.naacl-main)
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Jamar Sullivan Jr., Will Brackenbury, Andrew McNutt, Kevin Bryson, Kwam Byll, Yuxin Chen, Michael Littman, Chenhao Tan, Blase Ur
| Challenge: | In the context of data labeling, researchers are interested in having humans select rationales . |
| Approach: | They conducted an online user study to understand how humans select rationales . they found that participants were near unanimous in their data labels . |
| Outcome: | The results show that participants selected 12% of input tokens as rationales, but fewer if unable to drag over multiple tokens at once. |
InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)
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| 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 . |
A Computational Method for Measuring Open Codes in Qualitative Analysis (2026.findings-acl)
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John Chen, Alexandros Nikolaos Lotsos, Sihan Cheng, Lexie Zhao, Yanjia Zhang, Jessica Hullman, Bruce Sherin, Uri Wilensky, Michael Horn
| Challenge: | Qualitative analysis is widely adopted across many social science disciplines. |
| Approach: | They propose a theory-informed computational method for measuring inductive coding results from humans and GAI. |
| Outcome: | The proposed method captures breadth, consensus, unique contribution, and systematic deviation without assuming ground truth. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
Watermarking Large Language Models: An Unbiased and Low-risk Method (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have highlighted the risk of misusing them, raising the need for accurate detection of LLM-generated content. |
| Approach: | They propose a method to inject imperceptible identifiers into large language models (LLMs) this method is unbiased and preserves the original token distribution in expectation . |
| Outcome: | The proposed method preserves the original token distribution in expectation and has lower risk of producing unsatisfactory outputs in low-entropy scenarios compared to existing unbiased watermarks. |
REIC: RAG-Enhanced Intent Classification at Scale (2025.emnlp-industry)
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Ziji Zhang, Michael Yang, Zhiyu Chen, Yingying Zhuang, Shu-Ting Pi, Qun Liu, Rajashekar Maragoud, Vy Nguyen, Anurag Beniwal
| Challenge: | Accurate intent classification is critical for efficient routing in customer service . however, as companies expand their product lines, intent classification faces scalability challenges . |
| Approach: | They propose a retrieval-augmented generation Enhanced Intent Classification approach which leverages retrieval augmented generation to integrate relevant knowledge into a model. |
| Outcome: | The proposed approach outperforms fine-tuning, zero-shot, and few-shot methods on real-world datasets. |
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (2025.acl-demo)
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Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, Yen-Sung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Yu-Yen Cheng, Karen Farley, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A. Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, Jesse Dodge
| Challenge: | tracing language models' outputs back to training data is a problem because they are trained on text corpora with trillions of tokens . existing methods for tracers have not been scaled to work within this multi-trillion-token setting . |
| Approach: | They propose a system that traces language models' outputs verbatim back to training data . OLMOTRACE retrieves documents from the model's training data that contain exact matches . |
| Outcome: | The proposed system can find verbatim matches between LM output and training data . it can be used to explore fact checking, hallucination, and creativity of language models . |
GREEN: Generative Radiology Report Evaluation and Error Notation (2024.findings-emnlp)
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Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Md, Michael Moseley, Curtis Langlotz, Akshay Chaudhari, Jean-Benoit Delbrouck
| Challenge: | Existing automated evaluation metrics fail to consider factual correctness or are limited in their interpretability. |
| Approach: | They propose a radiology report evaluation metric that leverages natural language understanding of language models to identify and explain clinically significant errors. |
| Outcome: | The proposed method demonstrates higher correlation with expert error counts and higher alignment with expert preferences when compared to previous methods. |
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)
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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh
| Challenge: | Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts. |
| Approach: | They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy. |
| Outcome: | Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model. |
Lightweight Transformers for Conversational AI (2022.naacl-industry)
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| Challenge: | Commercial dialogue systems typically require a small footprint and fast execution time, but recent trends are in the other direction, resulting in difficulties in model deployment. |
| Approach: | They build Transformer-based Language Models from scratch on large corpora of conversational data and compare their performance against BERT and other strong baselines on dialogue probing tasks. |
| Outcome: | The proposed model outperforms existing models on dialogue probing tasks and can be fine-tuned on a single consumer GPU card. |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (2024.acl-long)
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| Challenge: | Existing methods to build language agents that can plan efficiently and accurately have not met the needs of advanced planning methods to achieve such improvements. |
| Approach: | They propose to use iterative correction and tree search to solve multi-step problems in a language agent framework with three components: a generator, a discriminator, and a planning method. |
| Outcome: | The proposed methods improve performance on two tasks, text-to-SQL parsing and mathematical reasoning, while using discriminators with 90% accuracy. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2023.acl-long)
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Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman
| Challenge: | Getting sociological beliefs wrong can slow research and lead to wasted effort, missed opportunities, and needless fights. |
| Approach: | They present the results of the NLP Community Metasurvey, run from May to June 2022. |
| Outcome: | The NLP community metasurvey elicited opinions on controversial issues from May to June 2022. |
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
Rewarding Coreference Resolvers for Being Consistent with World Knowledge (D19-1)
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Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard
| Challenge: | Unresolved coreference is a bottleneck for relation extraction systems . a state-of-the-art system may be able to infer the relation using distributional information about the phrase the Sunshine State, but is likely to have limited evidence for the decision that it is coreferential with Florida rather than with Skynyrd. |
| Approach: | They propose to forward coreference input to relation extraction system and reward them for producing triples that are found in knowledge bases. |
| Outcome: | The proposed approach improves over the state-of-the-art by forwarding their input to a relation extraction system and rewarding resolvers for producing triples that are found in knowledge bases. |
Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection (2026.eacl-long)
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| Challenge: | Existing computational approaches focus on logical structures of fallacies and argumentation schemes, ignoring the emotional dimension of argumentation. |
| Approach: | They propose to use large language models to systematically change emotional appeals in fallacious arguments by using a computational approach. |
| Outcome: | The proposed method reduces fallacy detection by 14.5% on average on human arguments with enjoyment over fear or sadness. |
A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling (2025.findings-emnlp)
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Shiyu Ji, Farnoosh Hashemi, Joice Chen, Juanwen Pan, Weicheng Ma, Hefan Zhang, Sophia Pan, Ming Cheng, Shubham Mohole, Saeed Hassanpour, Soroush Vosoughi, Michael Macy
| Challenge: | Rhetorical strategies are important to persuasive communication, but their analysis relies on human annotation, which is costly, inconsistent and difficult to scale. |
| Approach: | They propose a framework that leverages large language models to generate and label debate data . they fine-tune transformer-based classifiers on this dataset and validate it against human data a . |
| Outcome: | The proposed model achieves high performance and strong generalization across topical domains. |
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization (2023.acl-long)
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| Challenge: | a new benchmark summarization model is being developed to train few-shot summarizers . a large number of summarizing tasks are required to perform well in heterogeneous datasets. |
| Approach: | They propose a few-shot summarization model pre-trained with multiple summarizing tasks . they propose 'uniSumm' to be prefix-tuned to excel at any few-shot summarisation task . |
| Outcome: | The proposed model outperforms baseline models under automatic and human evaluations and achieves comparable results in human evaluation. |
Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information (2025.findings-emnlp)
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Weicheng Ma, Hefan Zhang, Shiyu Ji, Farnoosh Hashemi, Qichao Wang, Ivory Yang, Joice Chen, Juanwen Pan, Michael Macy, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Existing approaches to persuasive dialogue generation suffer from stance oscillation and low informativeness. |
| Approach: | They propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. |
| Outcome: | The proposed method ensures speaker characteristics guide all stages of dialogue generation and aligns language use with speakers’ native languages to better capture cultural nuances. |
MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)
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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang
| Challenge: | Existing work on controllable summarization with mixed attributes lacks designated annotations. |
| Approach: | They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources . |
| Outcome: | The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks . |
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning (2021.naacl-main)
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| 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. |
Text-to-SQL Error Correction with Language Models of Code (2023.acl-short)
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Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun
| Challenge: | Existing semantic parsers are not accurate enough for use in text-to-SQL parsing tasks. |
| Approach: | They propose to build clause-level edit models to correct SQL queries instead of token-level ones. |
| Outcome: | The proposed model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. |
Creator Context for Tweet Recommendation (2023.emnlp-industry)
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Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, Marc Najork, Matt Colen, Sergey Levi, Vladimir Ofitserov, Tanvir Amin
| Challenge: | In tweets, people refer to the content it delivers, but also to the person behind it. |
| Approach: | They examine how creator context can be used to advance tweet understanding by recommending relevant tweets to news articles. |
| Outcome: | The proposed model can improve a news article's relevance by recommending relevant tweets to news articles. |
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining (2022.naacl-industry)
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| Challenge: | Existing approaches to clustering unlabeled utterances are based on transformerbased sentence embedding methods. |
| Approach: | They propose a semantic embedding approach that can be leveraged to identify clusters of utterances that correspond to unhandled intents. |
| Outcome: | The proposed approach can identify clusters of utterances that correspond to unhandled intents from a large collection of enterprise virtual assistant data using a multi-task softmax loss. |
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence (2022.emnlp-main)
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| Challenge: | Existing work on question answering models relies on retrieved documents for provenance, but recent studies show that models can retain vast amounts of factual knowledge . retrieval-based generation approaches combine parametric knowledge sources with a large number of retrieved evidence documents, achieving state-of-the-art performance on open retrieval datasets. |
| Approach: | They propose to use parametric and parametric knowledge to generate free-form questions from retrieved evidence documents. |
| Outcome: | The proposed model can use parametric and parametric knowledge to generate free-form answers from retrieved evidence documents. |
The Source-Target Domain Mismatch Problem in Machine Translation (2021.eacl-main)
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Jiajun Shen, Peng-Jen Chen, Matthew Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc’Aurelio Ranzato
| Challenge: | Despite the interconnected world we live in, people in different places talk about different things in different parts of the world. |
| Approach: | They propose a metric to quantify the effect of local context in machine translation and propose measurable results. |
| Outcome: | The proposed metric can be used to quantify the effect of local context on the use of language in machine translation systems on low resource languages. |
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2022.findings-naacl)
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Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih
| Challenge: | a recent study aims to answer factual questions using a structured knowledge base (KBQA). |
| Approach: | They propose a unifying approach that homogenizes all knowledge sources by reducing them to text . they demonstrate that UniK-QA is a simple and yet effective way to combine heterogeneous sources of knowledge. |
| Outcome: | The proposed approach improves state-of-the-art results on knowledge-base QA tasks by 11 points compared to graph-based methods. |
Analyzing the Role of Semantic Representations in the Era of Large Language Models (2024.naacl-long)
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Zhijing Jin, Yuen Chen, Fernando Gonzalez Adauto, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona Diab
| Challenge: | Existing studies show the benefits of semantic representations in NLP tasks . Existing work using AMR is concerned with trainable models . |
| Approach: | They propose an AMR-driven chain-of-thought prompting method that uses AMR . they propose to use it to predict which input examples AMR may help or hurt on . |
| Outcome: | The proposed method hurts performance more than it helps on five different tasks. |
Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models (2024.naacl-long)
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| Challenge: | Vision-language models have demonstrated strong efficacy as visual assistants . however, evaluation of their reasoning capabilities requires a costly benchmark . |
| Approach: | They propose a pipeline to measure the reasoning consistency of vision-language models . they propose supervised fine-tuning of VLMs and feedback from LLMs . |
| Outcome: | The proposed framework reduces cost while ensuring the generation of a high-quality dataset. |
Prompt-based Conservation Learning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing multi-hop QA methods fail to answer a large fraction of sub-questions even if their parent questions are answered correctly. |
| Approach: | They propose a Prompt-based Conservation Learning framework that acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks. |
| Outcome: | The proposed framework acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks, mitigating forgetting. |
Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race? (2026.findings-acl)
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| Challenge: | Existing studies have focused on the models, neglecting the full deployment pipeline . previous studies have underestimated the practical success of these attacks . |
| Approach: | They evaluate the effectiveness of jailbreak attacks targeting LLM safety alignment . they highlight critical gaps and call for further refinement of detection accuracy and usability . |
| Outcome: | The proposed attacks can detect at least one safety filter across the entire deployment pipeline. |
AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)
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| 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. |
Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking (2023.eacl-main)
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Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, Michael Witbrock
| Challenge: | Existing scientific fact-checking datasets are limited due to expertise bottleneck . multi2Claim pipeline is a tool to convert multiple-choice questions into fact- checking data . |
| Approach: | They propose a pipeline for automatically converting multiple-choice questions into fact-checking data . they generate two large-scale datasets for scientific-fact-checker tasks . success at this task can help the reader understand scientific topics and promote science . |
| Outcome: | The proposed pipeline improves performance on two large-scale scientific fact-checking datasets. |
Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine (2026.findings-acl)
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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
| 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 . |
Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)
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| Challenge: | Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries. |
| Approach: | They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider . |
| Outcome: | The proposed learning scheme improves on a large-scale text-to-SQL data set. |
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. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |
Simple and Effective Unsupervised Speech Translation (2023.acl-long)
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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
| Approach: | They propose a pipeline approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis. |
| Outcome: | The proposed approach outperforms the state-of-the-art in unsupervised speech recognition by 3.2 BLEU on the Libri-Trans benchmark and the best supervised end-to-end models from only two years ago by an average of 5.0 BLUE over five X-En directions. |
Discovering Language Model Behaviors with Model-Written Evaluations (2023.findings-acl)
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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
| 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. |
Communication Makes Perfect: Persuasion Dataset Construction via Multi-LLM Communication (2025.naacl-long)
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Weicheng Ma, Hefan Zhang, Ivory Yang, Shiyu Ji, Joice Chen, Farnoosh Hashemi, Shubham Mohole, Ethan Gearey, Michael Macy, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. |
| Approach: | They propose a multi-LLM communication framework that facilitates the efficient production of high-quality, diverse linguistic content with minimal human oversight. |
| Outcome: | The proposed framework excels in naturalness, linguistic diversity, and the strategic use of persuasion, even in complex scenarios involving social taboos. |
Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity (2024.findings-emnlp)
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| Challenge: | Speculative decoding uses a small draft model to generate a single input token, instead of sequentially generating tokens until completion. |
| Approach: | They propose a method that generates varying draft models adapted to the input context using simple rules. |
| Outcome: | The proposed method is competitive with the current SOTA for self-speculative decoding while being a truly plug-and-play method. |
Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-long)
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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens
| Challenge: | a key intent behind many emails is to get a reply from the recipient. |
| Approach: | They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations. |
| Outcome: | The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates . |
Potato 2.0: A Comprehensive Annotation Platform with AI-in-the-Loop Support (2026.acl-demo)
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| Challenge: | Annotated data is still central to NLP and Generative AI, yet the demands on annotation have grown in both scale and complexity. |
| Approach: | They introduce Potato 2.0, an open source annotation platform for easy deployment and customization. |
| Outcome: | The new version of potato supports 39 different types of annotation tasks and multiple AI-assistance features. |
All Languages Matter: On the Multilingual Safety of LLMs (2024.findings-acl)
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| Challenge: | Existing safety benchmarks only concern the safety in one language, e.g. the majority language in the pretraining data such as English. |
| Approach: | They propose a prompting method to improve multilingual safety of ChatGPT by enhancing cross-lingual generalization of safety alignment. |
| Outcome: | The proposed method can significantly reduce the ratio of unsafe responses by 42% for non-English queries. |
TC-Bench: Benchmarking Temporal Compositionality in Conditional Video Generation (2025.findings-acl)
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| 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. |
CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)
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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
| 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. |
PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality (2026.findings-acl)
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| Challenge: | Increasing use of large language models (LLMs) in academic review has raised concerns about quality and fairness. |
| Approach: | They propose a framework to improve the quality of LLM-generated reviews by using retrieval-augmented generation. |
| Outcome: | The proposed framework improves the human-level quality of LLM-generated reviews by adopting prompt engineering and retrieval-augmented generation. |