Papers by Dan Liu

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

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

An Evaluation Mechanism of LLM-based Agents on Manipulating APIs (2024.findings-emnlp)

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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Bing Liu, Zhou Jianxiang, Dan Meng, Haonan Lu

Challenge:	Large Language Models (LLMs) have remarkable capabilities across a variety of tasks, such as language, mathematics, coding, and etc.
Approach:	They propose to decompose tool use capability into seven aspects and form a thorough evaluation schema for generic agents.
Outcome:	The proposed agent acts like a super-APP and can manipulate API-based tools.

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

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

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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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

Reasoning

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.

ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks (2024.findings-naacl)

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Xiaodong Yu, Hao Cheng, Xiaodong Liu, Dan Roth, Jianfeng Gao

Cross-Lingual Document Retrieval with Smooth Learning (2020.coling-main)

Challenge:	Existing static benchmarks do not guarantee that models can use the provided evidence for answering, which is essential to avoid hallucination when the required knowledge is new or private.
Approach:	They propose to automatically perturb existing static one for dynamic evaluation by using a chatGPT framework and a set of open-domain QA datasets.
Outcome:	The proposed framework generates new test cases on two open-domain QA datasets and is human-readable and useful to trigger hallucination in LLMs.

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Jiapeng Liu, Xiao Zhang, Dan Goldwasser, Xiao Wang

Information Retrieval

Challenge:	Cross-lingual document search is an information retrieval task in which the queries’ language and the documents’ language are different.
Approach:	They propose a robust framework that measures the relevance and a loss function that is a novel objective function.
Outcome:	The proposed framework achieves significant gains under commonly used ranking metrics on cross-lingual document retrieval task in a variety of languages.

Design Challenges for a Multi-Perspective Search Engine (2022.findings-naacl)

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Sihao Chen, Siyi Liu, Xander Uyttendaele, Yi Zhang, William Bruno, Dan Roth

Information Retrieval

Challenge:	a document retrieval system fails to deliver diverse and direct responses to controversial questions . classical document retrievals provide a ranked list of references to relevant but not necessarily trustworthy web documents .
Approach:	They propose a perspective-oriented document retrieval paradigm to address these challenges . they propose sponses with different perspectives within topically-related web documents .
Outcome:	The proposed system is based on a user survey and a prototype . it will be used to assess the utility and understanding of the system .

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

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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.

The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions (2023.emnlp-main)

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Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han

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

Challenge:	Recent advances in large language models (LLMs) have produced models that exhibit remarkable performance across a variety of NLP tasks.
Approach:	They analyze a large-scale collection of user-GPT conversations to identify a significant gap between academic research in NLP and the needs of real-world NLP applications.
Outcome:	The proposed model outperforms existing models in a large-scale collection of user-GPT conversations and identifies a significant gap between the tasks that users frequently request from LLMs and the tasks commonly studied in academic research.

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

In-Context Demonstration Selection with Cross Entropy Difference (2023.findings-emnlp)

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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Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu

A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)

Challenge:	Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks.
Approach:	They propose a cross-entropy difference method for selecting in-context demonstrations that uses parameter efficient finetuning to train small models on training data.
Outcome:	The proposed method outperforms baseline selection methods on a mix-domain dataset and shows that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example.

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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang

Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)

Challenge:	Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages.
Approach:	They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies.
Outcome:	The proposed model can be used to understand and generate human natural languages.

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Dan Su, Yan Xu, Genta Indra Winata, Peng Xu, Hyeondey Kim, Zihan Liu, Pascale Fung

Improving Pacing in Long-Form Story Planning (2023.findings-emnlp)

Challenge:	Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks.
Approach:	They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets.
Outcome:	The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively.

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Yichen Wang, Kevin Yang, Xiaoming Liu, Dan Klein

Challenge:	Existing systems for writing long-form stories suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details.
Approach:	They propose a system that uses a concreteness evaluator to judge which of two events is more concrete.
Outcome:	The proposed system improves pacing when automatically generating story outlines.

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

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

Open Domain Question Answering with Conflicting Contexts (2025.findings-naacl)

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.

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Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth

VisCGEC: Benchmarking the Visual Chinese Grammatical Error Correction (2025.naacl-long)

Challenge:	Open domain question answering systems often rely on information retrieved from large collections of text to answer questions.
Approach:	They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts .
Outcome:	The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training .

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Xiaoman Wang, Dan Yuan, Xin Liu, Yike Zhao, Xiaoxiao Zhang, Xizhi Chen, Yunshi Lan

Visual Data in NLP

Challenge:	Existing studies on Chinese grammatical error correction ignore multi-modality and faked errors, which pushes techniques far away from real-world scenarios.
Approach:	They propose to benchmark Chinese grammatical error correction for Chinese as a foreign language learner (CFL) using a dataset, they propose to use two CGEC frameworks to conduct experiments .
Outcome:	The proposed approach achieves an F 0.5 score of only 28.9%.

ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering (2026.acl-long)

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Marianne Menglin Liu, Daniel Garcia, Fjona Parllaku, Vikas Upadhyay, Fahad Shah, Dan Roth

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

Challenge:	Large language model (LLM) agents often face strict input context limits, preventing efficient consideration of large toolsets.
Approach:	They propose a tool that allows LLMs to merge tools with auto-correction and toolScopeRetriever to rank and select only the most relevant tools for each query.
Outcome:	Evaluations on three state-of-the-art LLMs and three open-source tool-use benchmarks show gains of 8.38% to 38.6% in tool selection accuracy.

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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

Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings (2021.findings-emnlp)

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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Kai Wang, Yu Liu, Dan Lin, Michael Sheng

Talking Point based Ideological Discourse Analysis in News Events (2025.findings-acl)

Challenge:	Recent knowledge graph embedding models based on hyperbolic geometry are complicated than Euclidean operations.
Approach:	They propose to use hyperbolic geometry to generate high-fidelity and parsimonious representations of hierarchical patterns in knowledge graphs.
Outcome:	The proposed models achieve state-of-the-art performance on two widely-used datasets and cost less than RotH.

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Nishanth Sridhar Nakshatri, Nikhil Mehta, Siyi Liu, Sihao Chen, Daniel Hopkins, Dan Roth, Dan Goldwasser

Discourse & Pragmatics

Challenge:	Existing models of ideological discourse analysis fail to capture the key elements that shape real-world narratives and lack the ability to integrate contextual information required for understanding abstract ideological views.
Approach:	They propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.
Outcome:	The proposed framework can generate ideology-specific viewpoints (partisan perspectives) it can be used to generate event snapshots, a visual way of interpreting event discourse.

Comparing Biases and the Impact of Multilingual Training across Multiple Languages (2023.emnlp-main)

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Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth

Sentiment Analysis

Challenge:	Currently, studies on bias and fairness in natural language processing focus on a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across languages for individual attributes.
Approach:	They adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for race, religion, nationality, and gender.
Outcome:	The proposed model favors groups that are dominant in each language's culture, indicating bias amplification, after multilingual finetuning.

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks (2026.findings-eacl)

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Haowei Fu, Bo Ni, Han Xu, Kunpeng Liu, Dan Lin, Tyler Derr

RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)

Challenge:	Large language models (LLMs) are the foundation of modern natural language processing, powering applications across diverse domains.
Approach:	They propose a model-agnostic defense framework which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM and a dedicated judge model to enhance resistance against membership inference attacks.
Outcome:	The proposed framework reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline while maintaining answer quality.

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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth

Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries (2023.findings-emnlp)

Challenge:	Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains.
Approach:	They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control .
Outcome:	The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control.

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Nishanth Nakshatri, Siyi Liu, Sihao Chen, Dan Roth, Dan Goldwasser, Daniel Hopkins

Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior (2020.findings-emnlp)

Challenge:	Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams.
Approach:	They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention.
Outcome:	The proposed framework produces more coherent clusters based on event summaries . the proposed framework is a first step in a new field of news analysis .

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Zi Lin, Jeremiah Liu, Zi Yang, Nan Hua, Dan Roth

GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)

Challenge:	Spectral-normalized identity priors (SNIP) is a structured pruning approach for a Transformer model.
Approach:	They propose a structured pruning approach which penalizes an entire residual module toward an identity mapping.
Outcome:	The proposed method improves on 5 GLUE benchmark tasks while maintaining comparable performance.

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Zhengxin Zhang, Chengyu Huang, Xufu Liu, Dan Zhao, Jinyan Su, Claire Cardie

Robustness in NLP

Challenge:	Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust .
Approach:	They propose to use GSM-Noise to refine inputs before engaging in in-depth analysis to improve LLM robustness under noisy conditions.
Outcome:	The proposed model can achieve consistent performance gains under noisy conditions with prompt engineering, supervised finetuning, and reinforcement learning.

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

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

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.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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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2025.acl-long)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

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Yifan Xu, Xiao Liu, Xueqiao Sun, Siyi Cheng, Hao Yu, Hanyu Lai, Shudan Zhang, Dan Zhang, Jie Tang, Yuxiao Dong

Conflicts in Texts: Data, Implications and Challenges (2025.findings-emnlp)

Challenge:	Existing studies on Android agents lack systematic research on open-source and closed-source models.
Approach:	They propose a framework for Android agents that includes an operation environment and a reproducible benchmark.
Outcome:	The proposed framework lifts the success rate of open-source LLMs and LMMs from 4.59% to 21.50% for LLM and 1.93% to 13.28% for LMM.

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Siyi Liu, Dan Roth

AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents (2025.emnlp-main)

Challenge:	Conflicts in data could reflect complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs.
Approach:	This survey categorizes conflicting information into three key areas . they identify the areas where conflicting data can be ignored and undermine models' reliability and trustworthiness.
Outcome:	The findings highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively.

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Fengze Liu, Haoyu Wang, Joonhyuk Cho, Dan Roth, Andrew Lo

Explainability & Interpretability in NLP

Challenge:	Clinical trials are expensive and time-consuming, and accurate trial prediction is key to advancing medical treatments.
Approach:	They propose a framework that combines reasoning capabilities of large language models with the explainability of classical machine learning to generate, evaluate, and refine tabular features without human input.
Outcome:	The proposed framework performs better than SOTA methods on clinical trial prediction tasks within a limited number of iterations.

ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition (2024.lrec-main)

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Ruizhe Huang, Mahsa Yarmohammadi, Jan Trmal, Jing Liu, Desh Raj, Leibny Paola Garcia, Alexei V. Ivanov, Patrick Ehlen, Mingzhi Yu, Dan Povey, Sanjeev Khudanpur

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (2023.findings-emnlp)

Challenge:	Existing work on contextual speech recognition (ASR) systems focuses on recognizing words that are not frequently seen in training data, such as rare words, but word error rate on rare words remains over 20%.
Approach:	They propose to use public-domain earnings calls and supplementary materials to evaluate contextual ASR approaches grounded on real-world applications.
Outcome:	The proposed frameworks are noisier than artificially synthesized contexts that contain the ground truth, yet still make great room for future improvement of contextual ASR technology.

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Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang

XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation (2021.emnlp-main)

Challenge:	Large language models can perform a wide range of tasks by following natural language instructions without task-specific fine-tuning.
Approach:	They propose a method to automatically improve the quality of LLM instructions . they leverage the generative ability of LMS to generate diverse candidate instructions based on a scoring model trained on 575 existing NLP tasks.
Outcome:	The proposed method surpasses human-written and LLM-generated instructions on 118 out-of-domain tasks.

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Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson

Multilinguality

Challenge:	Recent advances in multilingual natural language processing have improved performance on benchmarks such as XTREME and XGLUE by 13 points . however, improvements have been easier to achieve in some tasks than others .
Approach:	They extend XTREME to XTRAME-R, which includes ten natural language understanding tasks and covers 50 typologically diverse languages.
Outcome:	The proposed framework improves the performance on the XTREME multilingual benchmark by 13 points compared to human-level performance on English transfer learning.

Towards Long Context Hallucination Detection (2025.findings-naacl)

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Siyi Liu, Kishaloy Halder, Zheng Qi, Wei Xiao, Nikolaos Pappas, Phu Mon Htut, Neha Anna John, Yassine Benajiba, Dan Roth

Reasoning

Challenge:	Large language models are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context.
Approach:	They propose a dataset specifically designed for long-context hallucination detection.
Outcome:	The proposed architecture outperforms existing models while providing faster inference.

Character-Aware Models Improve Visual Text Rendering (2023.acl-long)

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Rosanne Liu, Dan Garrette, Chitwan Saharia, William Chan, Adam Roberts, Sharan Narang, Irina Blok, Rj Mical, Mohammad Norouzi, Noah Constant

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

Challenge:	Current image generation models struggle to produce well-formed visual text due to lack of character-level input features.
Approach:	They conduct a series of experiments to compare character-aware vs. character-blind text encoders to determine their spelling ability.
Outcome:	The character-aware models outperform character-blind models on a range of novel text rendering tasks.

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

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)

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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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu

Ranking and Sampling in Open-Domain Question Answering (D19-1)

Challenge:	Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning.
Approach:	They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking.
Outcome:	The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup.

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Yanfu Xu, Zheng Lin, Yuanxin Liu, Rui Liu, Weiping Wang, Dan Meng

Event Causality Identification with Synthetic Control (2024.emnlp-main)

Challenge:	Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing.
Approach:	They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph.
Outcome:	Experiments on three datasets show that the proposed model advances the state of the art.

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Haoyu Wang, Fengze Liu, Jiayao Zhang, Dan Roth, Kyle Richardson

Challenge:	Existing approaches to event causality identification have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality .
Approach:	They propose to use the Rubin Causal Model to identify event causality by generating a twin from existing corpora.
Outcome:	The proposed method can identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard.

Generation of Patient After-Visit Summaries to Support Physicians (2022.coling-1)

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Pengshan Cai, Fei Liu, Adarsha Bajracharya, Joe Sills, Alok Kapoor, Weisong Liu, Dan Berlowitz, David Levy, Richeek Pradhan, Hong Yu

Challenge:	After-visit summary is a summary note given to patients after their clinical visit.
Approach:	They propose to automate the generation of after-visit summaries and introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture important details of the clinical notes.
Outcome:	The proposed system improves on a large clinical dataset that contains electronic health record (EHR) notes and their associated summaries.

Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions (2021.acl-long)

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Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori Hashimoto

MultiOpEd: A Corpus of Multi-Perspective News Editorials (2021.naacl-main)

Challenge:	Despite extensive research showing the positive impact of uptake on student learning and achievement, there is little evidence that it is effective in teaching.
Approach:	They propose a framework for computationally measuring uptake by releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake . they formalize uptake as pointwise Jensen-Shannon Divergence (pJSD) and conduct a linguistically-motivated comparison of different unsupervised measures.
Outcome:	The proposed framework outperforms baseline measures in identifying uptake phenomena like question answering and reformulation.

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Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth

Challenge:	a news editorial is a genre of persuasive text where argumentation structure is usually implicit.
Approach:	They propose an open-domain news editorial corpus that supports automatic perspective discovery by identifying and abstracting natural language perspectives from editorials.
Outcome:	The proposed system supports automatic perspective discovery tasks in news editorials.

LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding (2026.acl-long)

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Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu, Mengqing Guo, Sujeeth Bharadwaj, Kyu J. Han, Tao Sheng, Sujith Ravi, Morteza Dehghani, Dan Roth

MetaMixSpeech: Meta Task Augmentation for Low-Resource Speech Recognition (2025.findings-emnlp)

Challenge:	Conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retrieve a fixed number of pages at inference.
Approach:	They propose a Layout-Aware Dynamic RAG framework that encodes content in isolated chunks during ingestion and retrieves a fixed number of pages at inference.
Outcome:	Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DoxVQA show that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning, and outperforming baseline retrievers by up to 20% in recall at comparable noise levels.

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Yaqi Chen, Hao Zhang, Wenlin Zhang, XuKui Yang, Dan Qu, Yunpeng Liu

InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT (2023.findings-emnlp)

Challenge:	Meta-learning has proven to be a powerful paradigm for improving speech recognition performance . however, multilingual meta learning also faces challenges such as task overfitting and learner overfit .
Approach:	a new method is proposed to augment meta-training tasks with "more data" the method incorporates both support and query augmentations .
Outcome:	The proposed method achieves a 6.35% improvement in the word error rate on FLEURS and Common Voice datasets.

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Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, Michael Zeng

Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset (2021.acl-long)

Challenge:	Recent advances in large language models have revolutionized the way summarization is generated.
Approach:	They propose a summarization model derived from GPT-3.5 through distillation that is compact and has comparable summarizing capabilities to GPT-3.
Outcome:	The proposed model outperforms the established best small models in prefix-tuning and full-data fine-tuned scenarios.

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Alexandra Ils, Dan Liu, Daniela Grunow, Steffen Eger

Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training (2023.eacl-main)

Challenge:	Using supervised machine learning, we assess how solidarity discourses changed before and during the COVID-19 crisis.
Approach:	They use social scientific concept of solidarity and its contestation, anti-solidarity, as problem setting to assess how European solidarity discourses changed before and during COVID-19.
Outcome:	The proposed model outperforms the baseline classifier with expert annotations by 25 points, from 58% macro-F1 to almost 85%.

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Wenliang Dai, Zihan Liu, Ziwei Ji, Dan Su, Pascale Fung

LMGQS: A Large-scale Dataset for Query-focused Summarization (2023.findings-emnlp)

Challenge:	Large-scale vision-language pre-trained (VLP) models generate unfaithful or nonsensical texts given the source input, which is called hallucination.
Approach:	They propose a VLP loss-based model to mitigate object hallucination by decoupling VLP objectives and a token-level image-text alignment.
Outcome:	The proposed model reduces object hallucination by 17.4% on two benchmarks.

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Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, Michael Zeng

Summarization

Challenge:	Lack of large-scale datasets for query-focused summarization hinders model development . lack of data limits the ability of QFS models to train robust neural models .
Approach:	They propose to generate a query for each summary sentence in a generic summarization annotation using a pretrained language model.
Outcome:	The proposed model achieves state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks.

Question Generation Based on Grammar Knowledge and Fine-grained Classification (2022.coling-1)

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Yuan Sun, Sisi Liu, Zhengcuo Dan, Xiaobing Zhao

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

Challenge:	Recent research on question generation has achieved great success, but some question types and answers did not match.
Approach:	They construct a question type classifier and a query generator to solve the problem of question types not matching with other questions.
Outcome:	The proposed model improves the accuracy of interrogative words in generated questions.

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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

Do Image–Text Metrics Respect Semantic Invariances? (2026.findings-acl)

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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Amit Agarwal, Hitesh Laxmichand Patel, Meizhu Liu, Jyotika Singh, Karan Dua, Hansa Meghwani, Matthew Rowe, M. Avendi, Yassi Abbasi, Tao Sheng, Sujith Ravi, Dan Roth

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

Challenge:	Reference-free image–to–text evaluators are now standard for scoring image–caption alignment, yet it is unclear whether they respect semantic invariances.
Approach:	They propose an invariance probe on five popular evaluators under semantics-preserving perturbations along three axes: spatial edits, object changes, and socio-linguistic framing.
Outcome:	The proposed invariance probe shows that spatial edits and simple phrasing changes shift scores by ()6% on average and cause ranking flips in up to (),37% of cases.

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

Information Retrieval

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.

Pretrained Transformers Improve Out-of-Distribution Robustness (2020.acl-main)

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Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, Dawn Song

Robustness in NLP

Challenge:	Pretrained Transformers are more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance.
Approach:	They construct a new robustness benchmark with real distribution shifts to measure out-of-distribution generalization for seven NLP datasets and compare them to previous models.
Outcome:	The proposed model generalizations for seven datasets show that pretrained Transformers are significantly less effective at detecting anomalous or OOD examples, while many previous models are often worse than chance.

G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment (2023.emnlp-main)

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Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu

Text Generation

Challenge:	Conventional reference-based metrics have low correlation with human judgments, especially for open-ended generation tasks.
Approach:	They propose to use large language models as reference-free NLG evaluators to assess the quality of NLG outputs.
Outcome:	The proposed framework outperforms all previous methods in two generation tasks, and has a Spearman correlation of 0.514 with human on summarization task, and a large variance in human judgments.

How Does In-Context Learning Help Prompt Tuning? (2024.findings-eacl)

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Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer

ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models (2024.acl-long)

Challenge:	a growing number of parameter-efficient adaptation methods are needed to fine-tune large language models.
Approach:	They propose a method that combines prompt tuning and in-context learning to improve prompt tuning by concatenating a natural language demonstration with learned prompt embeddings.
Outcome:	The proposed method outperforms prompt tuning and prompt tuning on five language generation tasks.

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Aparna Elangovan, Ling Liu, Lei Xu, Sravan Babu Bodapati, Dan Roth

StatsChartMWP: A Dataset for Evaluating Multimodal Mathematical Reasoning Abilities on Math Word Problems with Statistical Charts (2025.findings-emnlp)

Challenge:	In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the results are reliable.
Approach:	They propose a framework for human evaluation of generative large language models that takes into account usability, aesthetics and cognitive biases.
Outcome:	The proposed framework is based on the framework proposed by Deutsch and alnajjar . it is aimed at ensuring that human evaluation is accurate in the age of generative AI .

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Dan Zhu, Tianqiao Liu, Zitao Liu