Papers by Dan Li

How LLMs React to Industrial Spatio-Temporal Data? Assessing Hallucination with a Novel Traffic Incident Benchmark Dataset (2025.naacl-industry)

Challenge:	Existing work on robustness in text or code tasks has focused on classification, while robustness for code generation tasks is an uncharted area.
Approach:	They propose a robustness evaluation benchmark for code generation models that customizes over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format.
Outcome:	The proposed model performs better on human annotators and on SOTA models with human annnotators.

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Qiang Li, Mingkun Tan, Xun Zhao, Dan Zhang, Daoan Zhang, Shengzhao Lei, Anderson S. Chu, Lujun Li, Porawit Kamnoedboon

Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination? (2024.naacl-long)

Challenge:	Large language models (LLMs) have a high potential to digitize and enhance the health & public services industry.
Approach:	They propose to use a cross-lingual benchmark dataset to assess the robustness of state-of-the-art LLMs in the spatio vs temporal domain for traffic incident classification.
Outcome:	The proposed model performs well in the spatio-temporal domain and in the non-English context.

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Bangzheng Li, Ben Zhou, Fei Wang, Xingyu Fu, Dan Roth, Muhao Chen

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning (2025.findings-emnlp)

Challenge:	Existing large language models (LLMs) suffer from hallucinations and unfaithful reasoning due to keyword/entity biases.
Approach:	They propose a new probing method and benchmark to quantify this phenomenon by using a keyword/entity biases-based probing technique called EUREQA.
Outcome:	The proposed method achieves 62% accuracy on multi-hop and complex QA benchmarks.

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Shengyuan Wang, Jie Feng, Tianhui Liu, Dan Pei, Yong Li

Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have extensive world knowledge, but often generate inaccurate geospatial knowledge.
Approach:	They propose a framework for evaluation of large language models to mitigate hallucinations . they use Kahneman-Tversky Optimization to align LLMs with their reality .
Outcome:	The proposed evaluation framework uncovers hallucinations in 20 advanced LLMs.

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Yu Feng, Phu Mon Htut, Zheng Qi, Wei Xiao, Manuel Mager, Nikolaos Pappas, Kishaloy Halder, Yang Li, Yassine Benajiba, Dan Roth

Challenge:	Existing methods to gauge model’s uncertainty through self-consistency in responses to the target query are misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same query when answering a knowledge-preserving perturbation of the query.
Approach:	They propose a method that uses multi-agent interaction to estimate black-box LLMs' uncertainty.
Outcome:	The proposed method outperforms existing self-consistency based methods and improves hallucination detection.

Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)

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Yue Wang, Dan Qiao, Juntao Li, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang

RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks (2025.emnlp-industry)

Challenge:	Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data.
Approach:	They propose a hierarchical text classification framework that can achieve both label controllability and text diversity by extracting high-quality hierarchic label information.
Outcome:	The proposed method can achieve label controllability and text diversity by extracting high-quality hierarchical label information.

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Amit Agarwal, Hitesh Laxmichand Patel, Srikant Panda, Hansa Meghwani, Jyotika Singh, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, Dan Roth

AMO-Bench: Large Language Models Still Struggle in High School Math Competitions (2026.findings-acl)

Challenge:	Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development.
Approach:	They introduce a region-based score to quantify a dataset's reliance on global versus local visual information.
Outcome:	The proposed model-based score systematically compares model performance on image patches versus full images to determine if tasks require holistic image understanding or can be solved with partial or localized visual cues.

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Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma, Yehao Lin, Xinxuan Lv, Xuanlin Wang, Xiaoyu Li, Ziwen Wang, Xuezhi Cao, Xunliang Cai

Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model (2022.emnlp-main)

Challenge:	Existing benchmarks for mathematical reasoning are becoming less effective due to performance saturation.
Approach:	They propose to use a mathematical reasoning benchmark with Olympiad difficulty to evaluate top-tier LLMs.
Outcome:	The proposed benchmarks are cross-validated by experts to meet IMO difficulty standards and entirely original problems to prevent performance leakages from data memorization.

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Mingqi Li, Fei Ding, Dan Zhang, Long Cheng, Hongxin Hu, Feng Luo

Cross Attention Augmented Transducer Networks for Simultaneous Translation (2021.emnlp-main)

Challenge:	Existing methods for improving multilingual models did not focus on learning the semantic structure of representation.
Approach:	They propose a method to improve multilingual language models by aligning parallel sentences . they propose token-, word-, sentence- and structure-level alignment objectives .
Outcome:	The proposed method outperforms baseline models on XNLI, PAWS-X, and XQuAD . it obtains comparable performance on low-resource languages, the authors show .

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Dan Liu, Mengge Du, Xiaoxi Li, Ya Li, Enhong Chen

Text Generation

Challenge:	Existing approaches to simultaneous translation are limited by monotonic constraint . a novel architecture for simultaneous translation is proposed .
Approach:	They propose a cross attention-augmented transducer for simultaneous translation that optimizes both policies and translation models by expanding target sequences with blank symbols.
Outcome:	The proposed architecture achieves better latency-quality trade-offs than state-of-the-art approaches.

Taming LLMs with Gradient Grouping (2025.acl-long)

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Siyuan Li, Juanxi Tian, Zedong Wang, Xin Jin, Zicheng Liu, Wentao Zhang, Dan Xu

SceneGenAgent: Precise Industrial Scene Generation with Coding Agent (2025.acl-long)

Challenge:	a new study presents scaling with gradient grouping (SGG) the adaptive learning rate scaling approach is based on per-parameter statistics, which incurs memory overhead.
Approach:	They propose an optimizer wrapper that improves adaptive learning rate estimation by dynamic grouping and group-specific scaling.
Outcome:	The proposed algorithm improves learning rate estimation on diverse models with different model sizes and batch sizes.

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Xiao Xia, Dan Zhang, Zibo Liao, Zhenyu Hou, Tianrui Sun, Jing Li, Ling Fu, Yuxiao Dong

Event-Centric Natural Language Processing (2021.acl-tutorials)

Challenge:	Recent work on scene generation focuses on generating 3D scenes from textual descriptions . however, the task of generating industrial scenes with LLMs is complex and requires precise measurements and positioning .
Approach:	They propose an LLM-based agent for generating industrial scenes through C# code.
Outcome:	Experiments show that LLMs powered by SceneGenAgent exceed their original performance . the agent achieves 81.0% success rate in real-world industrial scene generation tasks .

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Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth

Challenge:	This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
Approach:	This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks.
Outcome:	This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks.

Weak-to-Strong Honesty Alignment via Learning-to-Rank Supervision (2025.findings-acl)

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YunfanXie YunfanXie, Lixin Zou, Dan Luo, Min Tang, Chenliang Li

When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning (2026.acl-long)

Challenge:	Existing approaches to enhance honesty with prompt engineering and fine-tuning are limited by annotated data.
Approach:	They propose a framework that enhances honesty through weak-to-strong generalization by training weak LLMs under weak supervision to improve their honesty.
Outcome:	The proposed framework improves honesty in large models even with limited label data.

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Yang Xiang, Yixin Ji, Ruotao Xu, Dan Qiao, Zheming Yang, Juntao Li, Min Zhang

Read before Generate! Faithful Long Form Question Answering with Machine Reading (2022.findings-acl)

Challenge:	Existing approaches to early exit reasoning often rely on handcrafted or empirical indicators that are unreliable and impractical.
Approach:	They propose a framework that allows LRMs to assess the sufficiency of its chain-of-thought and determine the optimal point for early exit.
Outcome:	The proposed framework reduces reasoning length by 28.9%–34.9% with minimal performance loss, effectively mitigating overthinking.

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Dan Su, Xiaoguang Li, Jindi Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Pascale Fung

Natural Language Interfaces

Challenge:	Long-form question answering (LFQA) generates a paragraph-length answer for a given question.
Approach:	They propose a framework that jointly models answer generation and machine reading.
Outcome:	The proposed model generates a more factually accurate answer from millions of documents retrieved from a large dataset.

MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety (2025.emnlp-main)

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Yahan Yang, Soham Dan, Shuo Li, Dan Roth, Insup Lee

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

Challenge:	Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking, which can elicit harmful or unsafe behaviors.
Approach:	They propose a multilingual guardrail with reasoning for prompt classification that integrates culturally and linguistically nuanced variants and supervised fine-tuning.
Outcome:	The proposed guardrail outperforms baselines across in-domain and out-of-domain languages by more than 15%.

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Visual Data in NLP

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

Neuronal Insights into LLM Attacks: Targeted Neuron Tuning for Precise and Robust Vulnerability Patching (2026.findings-acl)

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Dan Shi, Renren Jin, Zhuowen Han, Yuqi Ren, Xinwei Wu, Zhigen Li, Deyi Xiong

Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction (2024.findings-naacl)

Challenge:	Existing gradient-based attribution methods are inapplicable to adversarial attacks . et al.: Targeted neuron tuning improves model robustness against jailbreak attacks despite the model's vulnerability to jailbreak.
Approach:	They propose a gradient-based method to identify key neurons sensitive to adversarial behaviors in open-ended generation tasks.
Outcome:	The proposed method detects key neurons sensitive to adversarial behaviors in open-ended tasks.

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Boyi Li, Rodolfo Corona, Karttikeya Mangalam, Catherine Chen, Daniel Flaherty, Serge Belongie, Kilian Weinberger, Jitendra Malik, Trevor Darrell, Dan Klein

Suggest-Verify-Revise: A Three-Stage Document-Level Event Causality Identification with Narrative Consistency (2026.acl-long)

Challenge:	Recent studies show multimodal inputs can improve grammar induction, but weak textual baselines are needed for training.
Approach:	They use a fixed grammar family to compare multimodal grammar induction methods . they find multimodal inputs can improve grammar induction by grounding textual inputs to the visual world .
Outcome:	The proposed model outperforms weaker baselines on four benchmark datasets.

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Ya Su, Hu Zhang, Dan Qiao, YuJie Wang, Yunxiao Zhao, Yue Fan, Shike Li, Ru Li, Hongye Tan

Challenge:	Existing methods for document-level Event Causality Identification rely on local semantic similarity for independent event-pair discrimination . Existing approaches ignore the influence of the overall narrative backbone in the propagation of causal dependencies and the role differentiation of events within multi-cause/multi-effect structures.
Approach:	They propose a suggest-verify-revise approach for document-level Event Causality Identification with narrative consistency (SVRECI) they integrate heuristic causal suggestions generated by an LLM with structural suggestions derived from hypergraph modeling .
Outcome:	The proposed approach outperforms existing methods on event-storylines and Causal-TimeBank datasets.

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

SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)

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.

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Dan Qiao, Chenchen Dai, Yuyang Ding, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)

Challenge:	Existing methods to handle label noise in text classification tasks are limited to visual data.
Approach:	They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model.
Outcome:	The proposed method outperforms baselines on three types of text classification tasks on visual and textual data.

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Dan Li, Yang Yang, Hongyin Tang, Jiahao Liu, Qifan Wang, Jingang Wang, Tong Xu, Wei Wu, Enhong Chen

Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification (2021.findings-acl)

Challenge:	Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts.
Approach:	They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins .
Outcome:	The proposed method outperforms state-of-the-art models on six text matching benchmarks.

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Chang Li, Dan Goldwasser

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)

Challenge:	a new framework for political perspective detection is proposed to improve text training costs . current deep learning models lack the ability to focus on text span for bias detection .
Approach:	They propose a framework that pretrains the text model using social and linguistic contexts . they demonstrate that the framework improves performance by identifying bias-related text spans based on entity mentions and news sharing .
Outcome:	The proposed framework improves on two news bias datasets and improves performance on the general source and task.

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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

Scalable Patent Classification with Aggregated Multi-View Ranking (2024.lrec-main)

Challenge:	Existing methods for data-to-text generation focus on specific types of structured data.
Approach:	They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations.
Outcome:	The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data.

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Dan Li, Vikrant Yadav, Zi Long Zhu, Maziar Moradi Fard, Zubair Afzal, George Tsatsaronis

Structured Representation Learning for Online Debate Stance Prediction (C18-1)

Challenge:	Existing classification-based models struggle with scaling to large numbers of labels and generalizing to new labels.
Approach:	They propose a ranking-based method that integrates different views of patents and a meta-model that aggregates and ranks the labels given by the ranking models.
Outcome:	The proposed method outperforms the current state-of-the-art methods on two datasets . it can alleviate the limitations and achieve a significant performance improvement .

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Chang Li, Aldo Porco, Dan Goldwasser

Representation Learning

Challenge:	Existing models for understanding debate dialog ignore relationships between different topics and focus on textual content and user interaction.
Approach:	They propose to view this task as a representation learning problem and embed the text and authors jointly based on their interactions.
Outcome:	The proposed model can achieve significantly better results compared to competing models.

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

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

DataSciBench: An LLM Agent Benchmark for Data Science (2026.findings-acl)

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.

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Dan Zhang, Sining Zhoubian, Min Cai, Fengzu Li, Lekang Yang, Wei Wang, Tianjiao Dong, Ziniu Hu, Jie Tang, Yisong Yue

Challenge:	Existing benchmarks focus on single task, simple evaluation metrics, and readily available ground truth (GT) DataSciBench is built on curated, natural, and challenging prompts with complex evaluation criteria and uncertain GT.
Approach:	They propose a benchmark for evaluating Large Language Models in data science that integrates LLM-based self-consistency and human verification to ensure accuracy.
Outcome:	The proposed framework outperforms open-source models in all metrics and offers rigorous insights into LLM strengths and weaknesses.

PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications (2025.emnlp-industry)

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Hitesh Laxmichand Patel, Amit Agarwal, Srikant Panda, Hansa Meghwani, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, Dan Roth

Robustness in NLP

Challenge:	Existing evaluation metrics for Multimodal Large Language Models (MLLMs) are inadequate to assess their robustness to irrelevant or distracting visual context.
Approach:	They propose a patch-context-robustness index to measure MLLMs' robustness to visual context variations.
Outcome:	The proposed score measures the robustness of MLLMs to visual contexts across 15 vision-language benchmarks.

Recursive Routing Networks: Learning to Compose Modules for Language Understanding (N19-1)

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Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen

Challenge:	Recursive Routing Networks are modular, adaptable models that learn effectively in diverse environments.
Approach:	They propose to apply Recursive Routing Networks (RRNs) to natural language understanding by integrating them into existing architectures and recurrent network hidden layers.
Outcome:	The proposed model optimizes the parameters of the functions and the meta-learner decision-making component for routing inputs through those functions.

Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation (2023.emnlp-industry)

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Dan Li, Zi Long Zhu, Janneke van de Loo, Agnes Masip Gomez, Vikrant Yadav, Georgios Tsatsaronis, Zubair Afzal

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

Challenge:	Existing approaches to extreme multi-label text classification face inherent challenges in terms of model, data, and evaluation.
Approach:	They propose a label ranking model as an alternative to the conventional SciBERT-based classification model and an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system.
Outcome:	The proposed model enables efficient handling of large-scale labels and accommodates new labels.

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

Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media (P19-1)

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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Chang Li, Dan Goldwasser

Structured Data in NLP

Challenge:	Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task.
Approach:	They propose a neural architecture for representing relational information to capture social context of news documents.
Outcome:	The proposed model performs better than supervised models in the supervised setting and shows that it provides a distant supervision signal.

New Frontiers of Information Extraction (2022.naacl-tutorials)

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Muhao Chen, Lifu Huang, Manling Li, Ben Zhou, Heng Ji, Dan Roth

UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)

Challenge:	Information extraction (IE) is the process of automatically extracting structural information from unstructured or semi-structured data.
Approach:	This tutorial will provide an introduction to recent advances in IE by answering several important research questions.
Outcome:	The tutorial will address several important research questions and outline directions for further investigation.

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Huanqin Wu, Wei Liu, Lei Li, Dan Nie, Tao Chen, Feng Zhang, Di Wang

Devil’s Advocate: Anticipatory Reflection for LLM Agents (2024.findings-emnlp)

Challenge:	Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity.
Approach:	They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation.
Outcome:	The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction.

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Haoyu Wang, Tao Li, Zhiwei Deng, Dan Roth, Yang Li

Low-Resource NLP

Challenge:	Introspection-driven approach equips LLM agents with introspection, enhancing consistency and adaptability in solving complex tasks.
Approach:	They propose a zero-shot approach that equips LLM agents with introspection, enhancing consistency and adaptability in solving complex tasks.
Outcome:	The proposed approach improves performance and efficiency by reducing the number of trials and plan revisions by 45%.

FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation (2022.findings-emnlp)

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Hao Zhang, Nianwen Si, Yaqi Chen, Zhen Li, Tong Niu, Xukui Yang, Dan Qu

DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (2022.acl-short)

Challenge:	Existing methods to perform implicit knowledge transfer from machine translation to ST model are difficult because of the task complexity and data scarcity.
Approach:	They recommend a method which conducts explicit knowledge transfer from MT to ST model by fine and coarse granularity contrastive learning.
Outcome:	The proposed method improves the performance of the end-to-end speech translation model on all 8 languages.

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Zheng Li, Zijian Wang, Ming Tan, Ramesh Nallapati, Parminder Bhatia, Andrew Arnold, Bing Xiang, Dan Roth

Federated Retrieval Augmented Generation for Multi-Product Question Answering (2025.coling-industry)

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.

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Parshin Shojaee, Sai Sree Harsha, Dan Luo, Akash Maharaj, Tong Yu, Yunyao Li

WinSpot: GUI Grounding Benchmark with Multimodal Large Language Models (2025.acl-short)

Challenge:	Existing multi-domain RAG-QA approaches query all domains indiscriminately or rely on rigid resource selection.
Approach:	They propose a multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge.
Outcome:	The proposed framework improves multi-product knowledge-augmented QA performance on Adobe products.

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Zheng Hui, Yinheng Li, Dan Zhao, Colby Banbury, Tianyi Chen, Kazuhito Koishida

A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing GUI grounding data focuses on web-based elements, leaving a gap in real-world GUI interaction data for non-web applications.
Approach:	They propose a framework that leverages Large Language Models to generate large-scale GUI grounding data.
Outcome:	The framework validates and refines 5,000 GUI coordinate-instruction pairs and provides high-quality data for training and evaluating visual GUI agents.

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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang

PAR2-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering (2026.acl-industry)

Challenge:	Inductive reasoning is an important task for large language models (LLMs).
Approach:	They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation.
Outcome:	The proposed method improves inductive reasoning in large language models.

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Xingyu Li, Rongguang Wang, Yuying Wang, Mengqing Guo, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth

Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)

Challenge:	Multi-hop question answering is a practical bottleneck in industry applications . large language models (LLMs) fail frequently when evidence coverage is incomplete or reasoning trajectories drift .
Approach:	They propose a training-free two-stage framework that separates coverage from commitment . it performs breadth-first anchoring to build a high-recall evidence frontier . compared with IRCoT, it achieves 23.5% higher answer accuracy .
Outcome:	The proposed framework outperforms strong baselines in MHQA benchmarks and achieves 23.5% higher answer accuracy and 10.5% NDCG gains in retrieval quality.

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Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang

Mitigating Language Confusion through Inference-time Intervention (2025.coling-main)

Challenge:	Modern NLP workflows require different models for generation and embedding tasks.
Approach:	They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder.
Outcome:	The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps.

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Xie Yunfan, Lixin Zou, Dan Luo, Min Tang, Chenliang Li, Xiangyang Luo, Liming Dong

Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models (2024.acl-long)

Challenge:	Existing methods to address the problem of language confusion are incontext learning and supervised fine-tuning (SFT) however, they consume context window space and require extensive data collection.
Approach:	They propose a language-sensitive intervention that detects and assesses language confusion without additional complex mechanisms.
Outcome:	The proposed method detects language confusion and assesses content quality without additional complex mechanisms.

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Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia

MM-LLMs: Recent Advances in MultiModal Large Language Models (2024.findings-acl)

Challenge:	Existing methods to finetun large language models (LLMs) only update a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetune process.
Approach:	They propose quantized side tuing (QST) which quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the original weights.
Outcome:	The proposed method reduces the memory footprint of the model weights, optimizer states, and intermediate activations while reducing the memory requirements.

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Duzhen Zhang, Yahan Yu, Jiahua Dong, Chenxing Li, Dan Su, Chenhui Chu, Dong Yu

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	MultiModal Large Language Models (MM-LLMs) have undergone significant advances in the past year . traditional MM models incur substantial computational costs, especially when trained from scratch .
Approach:	They propose a taxonomy encompassing 126 MM-LLMs and summarize key training recipes to enhance their potency.
Outcome:	The proposed models preserve the reasoning and decision-making capabilities of LLMs and empower diverse range of MM tasks.

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya

Descriptive Prompt Paraphrasing for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)

Challenge:	Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA .
Approach:	They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities.
Outcome:	a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region .

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Dan Liu, Lin Li, Xiaohui Tao, Jian Cui, Qing Xie

Efficient Sparse Attention needs Adaptive Token Release (2024.findings-acl)

Challenge:	Current researches mainly work on either of two types of targets in a decentralized manner.
Approach:	They propose a model to perform sentiment polarity on a target jointly considering its corresponding multiple modalities including text, image, and others.
Outcome:	The proposed model performs well on four datasets spanning the above two target types and is prompt-based language modelling.

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Chaoran Zhang, Lixin Zou, Dan Luo, Xiangyang Luo, Zihao Li, Min Tang, Chenliang Li

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks, however, their ‘large’ scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability.
Approach:	They propose to release resources from caches and rebuild key-value states by a lightweight controller module to approximate an ideal top-K sparse attention.
Outcome:	The proposed method achieves a significant throughput improvement of 221.8% over full attention and a model with 7 billion tokens.

Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge (2023.findings-acl)

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Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera, Henghui Zhu, Yuhao Zhang, Alexander Hanbo Li, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Dan Roth, Bing Xiang

Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning (2026.acl-long)

Challenge:	Open-ended Visual Question Answering (VQA) requires models to reason over visual and natural language inputs using world knowledge.
Approach:	They propose a new VQA pipeline that deploys a generate-then-select strategy guided by world knowledge for the first time.
Outcome:	The proposed pipeline expands the knowledge coverage from in-domain training data by 4.1% on OK-VQA, without additional computation cost.

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Chongyuan Dai, Jinpeng Hu, Hongchang Shi, Zhuo Li, Dan Guo, Xun Yang, Meng Wang

Dialogue Systems & Conversational Agents

Challenge:	Recent reasoning-augmented LLMs have demonstrated impressive capabilities across a wide range of domains owing to their exceptional text understanding capabilities.
Approach:	They propose a Chinese psychological LLM that integrates empathy, psychological expertise, and reasoning.
Outcome:	The proposed model produces over 75k high-quality psychological questions paired with detailed rationales, generated through and iterative prompt-rationale optimization procedure, along with 73k empathetic dialogues.

SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)

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Junhan Shi, Yijia Zhu, Zhenning Shi, Dan Zhao, Qing Li, Yong Jiang

Unsupervised Dense Retrieval for Scientific Articles (2022.emnlp-industry)

Challenge:	Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains.
Approach:	They propose a collaborative framework that combines large and small models for effective reasoning.
Outcome:	The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference.

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Dan Li, Vikrant Yadav, Zubair Afzal, George Tsatsaronis