Papers by Dan Wang

PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents (2024.eacl-long)

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

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

Challenge:	Using chain-of-thought prompting, large language models perform better on complex reasoning tasks.
Approach:	They propose a prompting framework that decomposes a question into a sequence of actions and executes them over the document to obtain the answer.
Outcome:	The proposed framework outperforms zero-shot and chain-of-thought prompting on a QuALITY dataset . it proposes a plan based on actions mined from a training set and executes it step by step .

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

Reasoning

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.

CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)

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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun

Multilinguality

Challenge:	Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language.
Approach:	They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking.
Outcome:	The proposed approach mitigates language bias and consistently improves mRAG performance across languages.

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

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

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

Domain Generalization via Switch Knowledge Distillation for Robust Review Representation (2023.findings-acl)

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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You Zhang, Jin Wang, Liang-Chih Yu, Dan Xu, Xuejie Zhang

Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation (2025.findings-naacl)

Challenge:	Existing models for review representations of unseen or anonymous users are limited by their in-domain nature.
Approach:	They propose to use in-domain user and product information to generalize reviews . they use switch knowledge distillation to learn review representations for unseen users .
Outcome:	The proposed model performs well for existing or anonymous unseen users.

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Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Corey Curran, Keerthiram Murugesan, Soham Dan, Vibha Anand, Kristin Bennett

Event Semantic Classification in Context (2024.findings-eacl)

Challenge:	Clinical trials require baseline features to characterize participants and ensure accurate study outcomes.
Approach:	They evaluate LLMs' ability to generate appropriate baseline features for clinical trials . they use CT-Repo and CT-Pub datasets to generate features from clinical trials.
Outcome:	The proposed framework improves the performance of the baseline feature generation model on a clinical trial.

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Haoyu Wang, Hongming Zhang, Kaiqiang Song, Dong Yu, Dan Roth

WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning (2026.acl-long)

Challenge:	In this work, we focus on the semantic classification of events in context to help machines gain a deeper understanding of events.
Approach:	They propose to integrate event semantics into downstream tasks to help machines understand events better.
Outcome:	The proposed model improves the understanding of events in context.

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Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Duolin Sun, Meixiu Long, Yihan Jiao, Zhehao Tan, Jian Wang, Peng Wei, Jinjie Gu

Structured Data in NLP

Challenge:	Open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and exploration of unproductive branches.
Approach:	They propose a framework that compresses web agent trajectories via graph-based pruning.
Outcome:	The proposed framework reduces tool-call rounds by 20% while improving accuracy and efficiency while maintaining the same level of performance as existing models.

Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes (2023.emnlp-main)

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Haoyu Wang, Hongming Zhang, Yueguan Wang, Yuqian Deng, Muhao Chen, Dan Roth

Commonsense Reasoning

Challenge:	Existing models of event processing do not understand the essentiality of step events towards a goal event.
Approach:	They propose to deconstruct a goal event into a discrete representation of finer-grained (step) events, which are not equally important to the goal.
Outcome:	The proposed model can understand the essentiality of different step events towards a goal event.

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.

Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)

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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu

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

Challenge:	Sentence embeddings are typically learned to recognize the semantic relation between two text inputs.
Approach:	They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text.
Outcome:	The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences.

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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.

Convolutional Neural Network for Universal Sentence Embeddings (C18-1)

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Xiaoqi Jiao, Fang Wang, Dan Feng

Representation Learning

Challenge:	Recent studies show that averaging word embeddings is effective for NLP but these models represent a sentence only in terms of features of words or uni-grams.
Approach:	They propose a CNN-based model that uses both features of words and n-grams to encode sentences.
Outcome:	The proposed model performs better than existing models in transfer learning setting and exceeds state of the art in supervised learning setting by initializing the parameters with the pre-trained sentence embeddings.

Capturing the Content of a Document through Complex Event Identification (2022.starsem-1)

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Zheng Qi, Elior Sulem, Haoyu Wang, Xiaodong Yu, Dan Roth

Challenge:	Recent work grouped granular events into more general events, called complex events . however, this approach assumes that a given complex event is always described in consecutive sentences .
Approach:	They propose a context-augmented representation learning approach that uses contextual information to model pairwise relation between granular events.
Outcome:	The proposed approach outperforms baselines on the complex event identification task.

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

HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (2025.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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Yihan Jiao, Zhehao Tan, Dan Yang, Duolin Sun, Jie Feng, Yue Shen, Jian Wang, Peng Wei

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

Challenge:	In-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to inconsistent document quality and retrieval system imperfections.
Approach:	They propose that RAG models should possess three progressively hierarchical abilities: (1) Filtering: the ability to select relevant information; (2) Combination: the capability to combine semantic information across paragraphs; (3) RAG-specific reasoning: the capacity to further process external knowledge using internal knowledge.
Outcome:	Experiments show that the proposed method significantly improves the model’s open-book examination capability on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.

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

Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI (2026.findings-acl)

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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Xingmeng Zhao, Tongnian Wang, Dan Schumacher, Veronica Rammouz, Anthony Rios

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

Challenge:	Artificial intelligence (AI) is rapidly transforming healthcare but can also introduce risks, including bias, privacy violations, and unequal access.
Approach:	They propose a framework that generates speculative user stories and supports multi-agent discussions to help people reflect on potential benefits and harms of healthcare AI before deployment.
Outcome:	The framework generates speculative user stories and supports multi-agent discussions to help people reflect on potential benefits and harms of healthcare AI before deployment.

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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.

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)

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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth

Question Answering

Challenge:	Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive.
Approach:	They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C.
Outcome:	The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness.

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

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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.

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

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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%.

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

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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

Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)

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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Wenpeng Yin, Muhao Chen, Rui Zhang, Ben Zhou, Fei Wang, Dan Roth

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

Challenge:	general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data.
Approach:	This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks .
Outcome:	This tutorial aims to examine the evolution of general-purpose large language models (LLMs) the authors argue that the evolution is dependent on the availability of training data and the scale of the models.

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Kai Wang, Yu Liu, Dan Lin, Michael Sheng

RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.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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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth

From Mimesis to Metamorphosis: Evolving VLM Judges via In-Context Comparing and Knowledge Internalization (2026.findings-acl)

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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Juntuo Wang, Yuming Qiao, Yifan Yang, Lunxi Yuan, Liang Luo, Dan Meng

Information Retrieval

Challenge:	Existing approaches to subjective assessment are inconsistent and inconsistent due to inconsistent scales and inherent preference biases.
Approach:	They propose a framework that operationalizes subjective assessment as comparative analysis and internalizes it via Language Buttons.
Outcome:	The proposed framework achieves state-of-the-art performance across multiple benchmarks and is scale-steerable.

QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models (2024.emnlp-main)

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Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

Challenge:	Large Language Models (LLMs) are extremely popular, leading to a race towards reducing their inference costs.
Approach:	They propose a method that quantizes weights and activations to 4 bits to achieve better accuracy.
Outcome:	The proposed method reduces runtime costs in memory-bound models but does not address cost-bound scenarios.

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji

Dialogue Systems & Conversational Agents

Challenge:	a framework for constructing dialogue world models for natural language tasks is currently lacking.
Approach:	They propose a framework that can be used to train a dialogue world model.
Outcome:	The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification .

VAEGPT-Sim: Improving Sentence Representation with Limited Corpus Using Gradually-Denoising VAE (2024.findings-acl)

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Zhenyi Wang, Haiyan Ning, Qing Ling, Dan Wang

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

Challenge:	Text embedding requires a highly efficient method for training domain-specific models on limited corpora.
Approach:	They propose a model that combines a denoising variational autoencoder with a target-specific discriminator to generate synonymous sentences that closely resemble human language.
Outcome:	The proposed model surpasses ConSERT by 2.8 points in small-dataset training on STS benchmarks.

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

Joint Constrained Learning for Event-Event Relation Extraction (2020.emnlp-main)

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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Haoyu Wang, Muhao Chen, Hongming Zhang, Dan Roth

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

Challenge:	Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other.
Approach:	They propose a joint constrained learning framework that enforces logical constraints within and across multiple temporal and subevent relations of events by converting constraints into differentiable learning objectives.
Outcome:	The proposed framework outperforms SOTA methods on benchmarks for temporal relation extraction and event hierarchy construction.

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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

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding (2023.emnlp-main)

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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Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks

Reasoning

Challenge:	Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study.
Approach:	They propose a Merger Agreement Understanding Dataset with over 39,000 examples and over 47,000 annotations.
Outcome:	The Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study.

CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)

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Yifei Xu, Yingjie Zong, Wang Zhonghua, Sirui Wu, Yuan Rao, Dan Zhang, Shuiguang Deng

Challenge:	Current approaches to news writing rely on superficially retrieved information and oversimplified knowledge enumeration resulting in shallow, repetitive, and unordered outputs.
Approach:	They propose an LLM-based multi-agent controllable news writing framework called CtrlNews . they propose a fine-grained viewpoint control mechanism to regulate bias, emotion, and exaggeration attributes.
Outcome:	The proposed framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles.

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

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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

Generic Temporal Reasoning with Differential Analysis and Explanation (2023.acl-long)

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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Yu Feng, Ben Zhou, Haoyu Wang, Helen Jin, Dan Roth

Learning Constraints and Descriptive Segmentation for Subevent Detection (2021.emnlp-main)

Challenge:	Existing temporal reasoning models drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions.
Approach:	They propose a task called TODAY that evaluates whether systems can correctly understand the effect of incremental changes in temporal relation distributions.
Outcome:	The proposed task outperforms existing models, including GPT-3.5, on in-domain benchmarks while allowing for more appropriate annotations.

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Haoyu Wang, Hongming Zhang, Muhao Chen, Dan Roth

Challenge:	Event mentions in text correspond to real-world events of varying degrees of granularity . task of subevent detection aims to resolve this granulem issue by recognizing membership of events .
Approach:	They propose a task of event-based text segmentation as an auxiliary task to improve learning for subevent detection.
Outcome:	The proposed method outperforms baseline methods on subevent detection, HiEve and IC datasets while achieving decent performance on EventSeg prediction.

Taxonomy Expansion for Named Entity Recognition (2023.emnlp-main)

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Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini, Neha John, Shuai Wang, Yassine Benajiba, Vittorio Castelli, Dan Roth, Miguel Ballesteros

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

Challenge:	Training a Named Entity Recognition model involves fixing a taxonomy of entity types . however, requirements evolve and a model may need to recognize additional entity types.
Approach:	They propose a method that uses only partially annotated datasets to train a model to recognize additional entity types.
Outcome:	The proposed approach performs better with partially annotated datasets than other approaches . the gap between the proposed approach and other approaches is large in additional datasets .

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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.

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

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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.

Aligning to Constraints for Data-Efficient Language Model Customization (2025.findings-naacl)

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Fei Wang, Chao Shang, Shuai Wang, Sarthak Jain, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, Dan Roth

XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)

Challenge:	General-purpose language models (LMs) are aligned to diverse user intents, but fall short when it comes to specific applications.
Approach:	They propose a framework that uses constraints to automatically produce supervision signals for user alignment with constraints.
Outcome:	The proposed framework can produce supervision signals for user alignment with constraints.

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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar

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

Challenge:	Existing datasets are often informed by established research directions in the NLP community.
Approach:	They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks.
Outcome:	The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks.

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

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

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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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

Extending Multilingual BERT to Low-Resource Languages (2020.findings-emnlp)

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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Zihan Wang, Karthikeyan K, Stephen Mayhew, Dan Roth

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

Challenge:	Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning.
Approach:	They propose a simple but effective approach to extend multilingual BERT to any new language and show an increase in F1 on M-BERT and new languages.
Outcome:	The proposed approach improves on languages already in M-BERT and out of it on other languages.

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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%.

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

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

Natural Language Interfaces

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.

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

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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.

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

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

Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)

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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Chen Wang, Lai Wei, Yanzhi Zhang, Chenyang Shao, Zedong Dan, Weiran Huang, Ge Lan, Yue Wang

Zero-Shot On-the-Fly Event Schema Induction (2023.findings-eacl)

Challenge:	Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability.
Approach:	They propose a framework that provides targeted mechanisms for exploration and stabilization.
Outcome:	The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably.

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Rotem Dror, Haoyu Wang, Dan Roth

Analogous Process Structure Induction for Sub-event Sequence Prediction (2020.emnlp-main)

Challenge:	a new approach to event processing uses large language models to generate source documents that can be curated without manual data collection.
Approach:	They propose a framework that generates a graphical representation of events in documents . they show that the model is more complete than previous supervised methods .
Outcome:	The proposed model is more complete than human-curated schemas in most scenarios.

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Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song, Dan Roth

Challenge:	Existing work on event understanding is focusing on procedural (or horizontal) tasks such as predicting the next event given an observed sequence.
Approach:	They propose an Analogous Process Structure Induction framework which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub- sequence of previously unseen open-domain processes.
Outcome:	The proposed framework can predict the whole sub-event sequence of previously unseen open-domain processes.

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

Enough Coin Flips Can Make LLMs Act Bayesian (2025.acl-long)

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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Ritwik Gupta, Rodolfo Corona, Jiaxin Ge, Eric Wang, Dan Klein, Trevor Darrell, David M. Chan

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

Challenge:	Large language models exhibit the ability to generalize given few-shot examples in their input prompt, an emergent capability known as in-context learning.
Approach:	They investigate whether large language models use in-context learning to generalize given few-shot examples in their input prompt.
Outcome:	The proposed model can generalize given few-shot examples in their input prompt, an emergent capability known as in-context learning.

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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

Information Retrieval

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.

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

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

Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)

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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Zhe Dong, Jianmo Ni, Dan Bikel, Enrique Alfonseca, Yuan Wang, Chen Qu, Imed Zitouni

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

Challenge:	Dual encoders have been used for question-answering and information retrieval tasks with good results.
Approach:	They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers .
Outcome:	The proposed architectures outperform SDE and ADE on QA retrieval tasks.

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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.

CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context (2024.lrec-main)

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Yangruibo Ding, Zijian Wang, Wasi U. Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, Bing Xiang

Text Generation

Challenge:	Pre-trained language models (LMs) for code have shown promising performance in code completion tasks but ignore the rich semantics in other files within the same project.
Approach:	They propose a framework that jointly learns the in-file and cross-file context on top of code LMs and a static-analysis-based tool that locates and retrieves the most relevant project-level cross- file context for code completion.
Outcome:	The proposed framework improves existing code LMs with a 33.94% relative increase in exact match and 28.69% in identifier matching when the cross-file context is provided.

Zero-shot Label-Aware Event Trigger and Argument Classification (2021.findings-acl)

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Hongming Zhang, Haoyu Wang, Dan Roth

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

Challenge:	Existing work on event extraction relies on labor-intensive annotation, ignoring semantic meaning of event types' labels.
Approach:	They propose a zero-shot event extraction approach that first identifies events with existing tools and then maps them to a given taxonomy of event types in a no-shot manner.
Outcome:	The proposed approach doubles the performance of previous approaches on a ACE-2005 dataset . it leverages label representations induced by pre-trained language models and maps events to the target types .

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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

HMCL: Task-Optimal Text Representation Adaptation through Hierarchical Contrastive Learning (2025.findings-emnlp)

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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Zhenyi Wang, Yapeng Jia, Haiyan Ning, Peng Wang, Dan Wang, Yitao Cao

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

Challenge:	Hierarchical Multilevel Contrastive Learning (HMCL) improves text representation for general large language models.
Approach:	a new contrastive learning framework is developed to improve general large language models . HMCL integrates 3-level semantic differentiation and unifies contrastive and pair classification into a strategy .
Outcome:	HMCL outperforms unsupervised methods and supervised fine-tuning approaches in multi-domain and multilingual benchmarks.

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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

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction (2023.eacl-main)

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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Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob Gardner, Dan Roth, Muhao Chen

Improving Personalized Sentiment Representation with Knowledge-enhanced and Parameter-efficient Layer Normalization (2024.lrec-main)

Challenge:	Existing models for event temporal relation extraction are based on data-driven machine learning . however, TEMPREL extraction is not accurate under distribution shifts.
Approach:	They propose to conduct counterfactual analysis to attenuate the effects of two types of training biases: the event trigger bias and the frequent label bias.
Outcome:	The proposed model extracts TempRel and timelines more faithfully compared to SOTA methods . it is based on two perspectives: one is to extract genuinely based upon contextual description . the other is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text .

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You Zhang, Jin Wang, Liang-Chih Yu, Dan Xu, Xuejie Zhang

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

Challenge:	Existing studies on personalized sentiment classification consider document reviews as overall text unit and incorporate backgrounds (i.e., user and product information) Existing methods for personalized sentiment modeling have quadratic costs that increase with text length and heterogeneous mixes of background information and textual information.
Approach:	They propose a knowledge-enhanced and parameter-efficient layer normalization model that leverages pretrained checkpoints and background information into transformer structures.
Outcome:	The proposed model can be used to improve pretrained language models in document reviews and incorporate background information with parameter-efficient fine-tuning and knowledge injecting.

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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.

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.

A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners (2024.emnlp-main)

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Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie Su, Camillo Taylor, Dan Roth