Papers by Le Chen

ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search (2025.acl-long)

Challenge:	Extensive experiments on text datasets demonstrate that XAIFooler significantly outperforms all baselines by large margins in its ability to manipulate LIME’s explanations with high semantic preservability.
Approach:	They propose to use LIME to establish a baseline and then propose an algorithm to perturb text inputs and manipulate explanations.
Outcome:	The proposed algorithm outperforms baselines on text datasets and achieves high semantic preservability.

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Yize Zhang, Tianshu Wang, Sirui Chen, Kun Wang, Xingyu Zeng, Hongyu Lin, Xianpei Han, Le Sun, Chaochao Lu

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

Challenge:	Large language models (LLMs) have impressive capabilities but their application in open-ended, knowledge-intensive, complex reasoning scenarios is limited.
Approach:	They propose a framework that integrates risk assessment of intermediate reasoning states with dynamic retrieval-augmented generation within a Monte Carlo tree search paradigm.
Outcome:	The proposed framework outperforms the state-of-the-art KAR methods by up to 23.10% and the latest RAG-equipped large reasoning models by upto 25.37%.

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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.

Reducing cohort bias in natural language understanding systems with targeted self-training scheme (2023.acl-industry)

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Dieu-thu Le, Gabriela Hernandez, Bei Chen, Melanie Bradford

Symbol tuning improves in-context learning in language models (2023.emnlp-main)

Challenge:	In deep learning models, it is hard to capture all the variations of the language that different users can use.
Approach:	They propose a framework that uses four active learning strategies to identify important samples coming from new users and a self training phase where a teacher model is trained from the first phase to expand the training data with relevant cohort utterances.
Outcome:	The proposed framework reduces the bias related to new customers in a digital voice assistant system by using two phases: a fixing phase and a self training phase.

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Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc Le

Learning In-context Learning for Named Entity Recognition (2023.acl-long)

Challenge:	Language models are sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner.
Approach:	They propose to fine tune language models on in-context input-label pairs where natural language labels are replaced with arbitrary symbols.
Outcome:	The proposed model is much stronger at reasoning tasks and more robust to underspecified prompts than the standard model.

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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun

Facebook AI’s WAT19 Myanmar-English Translation Task Submission (D19-52)

Challenge:	Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations.
Approach:	They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances.
Outcome:	The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors.

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Peng-Jen Chen, Jiajun Shen, Matthew Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato

Machine Translation

Challenge:	Using back-translation, we can improve generalization by using noisy channel re-ranking and ensembling.
Approach:	They propose to use BPE-based transformer models to leverage monolingual data to improve generalization and use noisy channel re-ranking and ensembling to improve results.
Outcome:	The proposed system improves on the baseline system trained exclusively on the provided small parallel dataset, and the human evaluation and BLEU score are higher.

REInstruct: Building Instruction Data from Unlabeled Corpus (2024.findings-acl)

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Shu Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Probing Simile Knowledge from Pre-trained Language Models (2022.acl-long)

Challenge:	Existing methods for annotating instruction data are expensive and difficult to scale.
Approach:	They propose a method to automatically build instruction data from an unlabeled corpus without heavy reliance on proprietary LLMs and human annotation.
Outcome:	The proposed method outperforms existing methods on AlpacaEval leaderboard and other open-source methods.

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Weijie Chen, Yongzhu Chang, Rongsheng Zhang, Jiashu Pu, Guandan Chen, Le Zhang, Yadong Xi, Yijiang Chen, Chang Su

MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)

Challenge:	Existing approaches to learn generic knowledge from a large corpus are time-consuming and labor-intensive.
Approach:	They propose a framework to probe simile knowledge from pre-trained language models to solve SI and SG tasks.
Outcome:	The proposed framework solves the SI and SG tasks in a simile triple completion task.

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Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han

Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation (2025.acl-long)

Challenge:	Recent LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs and increasing compute cost and memory overhead.
Approach:	They propose an agent framework that maintains a compact memory during multi-turn interactions.
Outcome:	The proposed framework outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions.

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Fan Yin, Zifeng Wang, I-Hung Hsu, Jun Yan, Ke Jiang, Yanfei Chen, Jindong Gu, Long Le, Kai-Wei Chang, Chen-Yu Lee, Hamid Palangi, Tomas Pfister

Beyond Surface-Level Pattern Trap: LLM Agents for Faster and Smarter Cross-Architecture Code Migration (2026.findings-acl)

Challenge:	Large language models have been shown to be effective in multi-turn interactions . however, their performance may be limited in complex, multi-turned interactions involving users and multiple tools.
Approach:	They propose a framework for synthesizing high-quality training trajectories to enhance the function calling capability of large language model agents in multi-turn conversations with humans.
Outcome:	The proposed model outperforms the teacher model by 68.01 on BFCL-v3 and 73.30 on ToolQuery.

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Weijia Li, KE Gao, Pengfei Chen, Jiajie Li, Xinyu Wang, Yiran Le, Yize Wu, Ling Li

Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (P19-1)

Challenge:	cross-architecture code migration is a resource-intensive and errorprone task.
Approach:	a framework for cross-architecture code migration is proposed to decouple implementation details through functional mining and code refactoring.
Outcome:	a new framework improves performance and correctness over state-of-the-art frameworks on OpenCV migration tasks.

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Hung Le, Doyen Sahoo, Nancy Chen, Steven Hoi

Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)

Challenge:	Existing work on video-grounded dialogue systems is limited by feature space and semantic information.
Approach:	They propose multimodal transformer networks to encode videos and incorporate information from different modalities.
Outcome:	The proposed system generates appropriate conversational response to queries of humans based on visual and audio aspects of a given video . it also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance.

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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang

Challenge:	Recent studies have focused on a single pass of lyrics generation with little human intervention.
Approach:	They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes.
Outcome:	The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly.

DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)

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Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Challenge:	Existing presentation agents rely on predefined workflows and fixed templates to generate presentations.
Approach:	They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation.
Outcome:	The proposed framework can be used to generate presentations with environmental observations.

PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection (2025.naacl-long)

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Jooyoung Lee, Toshini Agrawal, Adaku Uchendu, Thai Le, Jinghui Chen, Dongwon Lee

Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)

Challenge:	Recent studies have raised concerns about the potential threats large language models pose to academic integrity and copyright protection.
Approach:	They propose a dataset of 46.5K synthetic text pairs that represent three major types of plagiarism: verbatim copying, paraphrasing, and summarization.
Outcome:	The proposed dataset shows that GPT-3.5 Turbo can produce high-quality paraphrases and summaries without significantly increasing text complexity compared to GPT-4 Turbo.

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Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)

Challenge:	Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances.
Approach:	They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm.
Outcome:	The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts.

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Ying Zhou, Xinyao Wang, Yulei Niu, Yaojie Shen, Lexin Tang, Fan Chen, Ben He, Le Sun, Longyin Wen

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing (2026.acl-long)

Challenge:	Recent advances in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis.
Approach:	They propose a controllable data synthesis framework based on variational autoencoder which leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution.
Outcome:	The proposed framework generates high-quality data with performance exceeding that of real data by 2%–7% on seven real-world datasets.

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Zhuoqun Li, Xuanang Chen, Hongyu Lin, Yaojie Lu, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding (2021.acl-long)

Challenge:	Existing paper search systems lack detailed information to support finer-grained queries.
Approach:	They propose a paper-based index that transforms abstract-based corpus index into hierarchical index tree and offline can support paper search queries.
Outcome:	The proposed system achieves the SOTA performance and excels in fine-grained scenarios.

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Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai

Semantic-aware Contrastive Learning for More Accurate Semantic Parsing (2022.emnlp-main)

Challenge:	Experimental results show that Synchronous Semantic Decoding (SSD) can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.
Approach:	They propose an unsupervised method which solves the semantic gap and the structure gap by leveraging paraphrasing and grammar-constrained decoding.
Outcome:	The proposed method can solve the semantic gap and structure gap on multiple datasets.

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Shan Wu, Chunlei Xin, Bo Chen, Xianpei Han, Le Sun

Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)

Challenge:	Existing studies on semantic parsing use Maximum Likelihood Estimation (MLE) to train discriminative semantic parses.
Approach:	They propose a semantic-aware contrastive learning algorithm which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration.
Outcome:	The proposed algorithm improves on two standard datasets and gets state-of-the-art performance over existing methods.

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Bo Chen, Le Sun, Xianpei Han

Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)

Challenge:	Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains.
Approach:	They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process.
Outcome:	The proposed model achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.

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Guozhao Mo, Yanjiang Liu, Yafei Shi, Jiawei Chen, Yang Li, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Bo Zheng, Xianpei Han

Linguistics & Cognitive NLP

Challenge:	Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models.
Approach:	They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration.
Outcome:	The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

Multimodal Dialogue State Tracking (2022.naacl-main)

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Hung Le, Nancy Chen, Steven Hoi

Few-shot Named Entity Recognition with Self-describing Networks (2022.acl-long)

Challenge:	Dialogue state tracking is a key component of dialogue systems.
Approach:	They propose to extend the definition of dialogue state tracking to multimodality . they propose a new synthetic benchmark and a novel baseline for this task .
Outcome:	The proposed task is based on a synthetic benchmark and a self-supervised video understanding task.

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Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun

ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch (2025.emnlp-main)

Challenge:	Existing few-shot named entity recognition (NER) models capture information from limited instances while transferring useful knowledge from external resources.
Approach:	They propose a self-describing mechanism for few-shot NER which can universally describe mentions using concepts and automatically map novel entity types to concepts.
Outcome:	The proposed model can universally describe mentions using concepts and automatically map novel entity types to concepts and adaptively recognize entities on-demand.

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Jiawei Chen, Xinyan Guan, Qianhao Yuan, Mo Guozhao, Weixiang Zhou, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Xianpei Han

Dialogue Systems & Conversational Agents

Challenge:	Existing instruction data synthesis methods focus on single-turn instructions and neglect cross-turn coherence, resulting in context drift and reduced task completion rates.
Approach:	They propose a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent.
Outcome:	The proposed framework outperforms existing models trained on single-turn and multi-turn instruction datasets.

VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems (2022.naacl-main)

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Hung Le, Nancy Chen, Steven Hoi

Reverse Thinking Makes LLMs Stronger Reasoners (2025.naacl-long)

Challenge:	Neural module networks (NMN) have been used in image-grounded tasks such as Visual Question Answering (VQA) however, very limited work on NMN has been studied in the video-ground dialogue tasks.
Approach:	They propose to use video as the grounding feature in video-grounded dialogues to model the information retrieval process in videogrounded language tasks as a pipeline of neural modules.
Outcome:	The proposed model can achieve promising performance on video-grounded dialogue and QA benchmarks.

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Justin Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister

Unsupervised training data re-weighting for natural language understanding with local distribution approximation (2022.emnlp-industry)

Challenge:	Reverse-Enhanced Thinking (RevThink) is a framework for large language models to perform reverse thinking.
Approach:	They propose a framework for enhancing forward-backward reasoning by collecting data from a teacher model and employing three objectives to train a student model in a multi-task learning fashion.
Outcome:	The proposed framework outperforms a fine-tuning method trained on 10x more forward reasoning on 12 datasets covering commonsense, math, and logical reasoning.

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Jose Garrido Ramas, Dieu-thu Le, Bei Chen, Manoj Kumar, Kay Rottmann

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025.acl-long)

Challenge:	a distribution mismatch between offline training and live data can cause biases . cyclic seasonality shifts, and changing pool of users can contribute to this problem .
Approach:	They propose an unsupervised approach to mitigate offline training data sampling bias . they propose a local distribution approximation in the pre-trained embedding space .
Outcome:	The proposed approach mitigates the offline training data sampling bias in multiple NLU tasks without additional annotation.

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Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Rajan Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister

Improving Robustness of Language Models from a Geometry-aware Perspective (2022.findings-acl)

Challenge:	Existing approaches to long-term dialogue memory management fail to capture the natural semantic structure of conversations, leading to fragmented and incomplete representations.
Approach:	They propose a mechanism that integrates forward- and backward-looking reflections into a personalized memory bank for effective future retrieval.
Outcome:	The proposed mechanism outperforms state-of-the-art benchmarks on a long-term dialogue memory model.

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Bin Zhu, Zhaoquan Gu, Le Wang, Jinyin Chen, Qi Xuan

Robustness in NLP

Challenge:	Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness.
Approach:	They propose friendly adversarial data augmentation and geometry-aware adversarial training to achieve stronger robustness using fewer search steps.
Outcome:	The proposed method can obtain stronger robustness using fewer steps than existing methods.

PRP-Graph: Pairwise Ranking Prompting to LLMs with Graph Aggregation for Effective Text Re-ranking (2024.acl-long)

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Jian Luo, Xuanang Chen, Ben He, Le Sun

PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving (2025.emnlp-main)

Challenge:	Existing methods for pairing ranking prompting only output the same label for comparison results of different confidence intervals without considering the uncertainty of pairwise comparison.
Approach:	They propose a pairwise ranking prompting approach that exploits the output probabilities of target labels to capture the degree of certainty of comparison results.
Outcome:	The proposed method shows strong robustness and acceptable efficiency on the BEIR benchmark.

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Mihir Parmar, Xin Liu, Palash Goyal, Yanfei Chen, Long Le, Swaroop Mishra, Hossein Mobahi, Jindong Gu, Zifeng Wang, Hootan Nakhost, Chitta Baral, Chen-Yu Lee, Tomas Pfister, Hamid Palangi

TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)

Challenge:	Existing methods for natural planning lack constraint-guided iterative verification and adaptive selection . a recent study found that LLMs are not good at such planning.
Approach:	They propose a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents.
Outcome:	The proposed framework improves inference-time algorithms on NATURAL PLAN and OlympiadBench benchmarks.

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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen

Robustness in NLP

Challenge:	Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society.
Approach:	They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system .
Outcome:	The proposed system simulates trending topics under poisoning attacks on social media platforms.

RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)

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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao

Green & Sustainable NLP

Challenge:	Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools.
Approach:	They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment.
Outcome:	The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool.

Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention (2021.emnlp-main)

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Jiawei Chen, Hongyu Lin, Xianpei Han, Le Sun

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)

Challenge:	Recent supervised ED approaches have achieved promising performance but require large number of manually annotated event data.
Approach:	They propose to overfit the trigger confounder of the context and the result . they propose to intervene on the context via backdoor adjustment during training .
Outcome:	The proposed method significantly improves the FSED on ACE05 and MAVEN datasets.

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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)

Challenge:	Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed .
Approach:	They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision.
Outcome:	The proposed framework reduces token usage while improving accuracy on math benchmarks.

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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun

UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues (2020.emnlp-main)

Challenge:	Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem.
Approach:	They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules.
Outcome:	The results show that pre-trained language models are forgetful and pre-training leads to retentive models .

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Hung Le, Doyen Sahoo, Chenghao Liu, Nancy Chen, Steven C.H. Hoi

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to training dialogue agents separately are not optimized for multi-domain task-oriented dialogues.
Approach:	They propose a unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues that jointly trains a bi-level state tracker and a joint dialogue act and response generator.
Outcome:	The proposed system outperforms existing systems on the MultiWOZ2.1 benchmark in dialogue state tracking, context-to-text, and end-to end settings.

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-Augmented Code Generation (2026.findings-acl)

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Qiming Zhu, Jialun Cao, Xuanang Chen, Weili Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung

Programming Languages in NLP

Challenge:	Current research on large language models with retrieval-augmented code generation (RACG) has focused on single-language settings, leaving their cross-lingual effectiveness underexplored.
Approach:	They construct a dataset covering 13 PLs with nearly 14K instances to study cross-lingual code knowledge transfer in RACG.
Outcome:	The proposed model shows unequal cross-lingual knowledge transfer even with direct injection and shows limited reliance on natural language information embedded in code when equipped with a code-specific retriever.

BIG-Bench Extra Hard (2025.acl-long)

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Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat

Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing (C18-1)

Challenge:	Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies.
Approach:	They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning .
Outcome:	The new benchmark pushes the boundaries of LLM reasoning evaluation.

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Bo Chen, Bo An, Le Sun, Xianpei Han

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation (2026.acl-long)

Challenge:	Semantic parsers rely on accurate and high-coverage lexicons, but they often use annotated logical forms to learn the lexic.
Approach:	They propose a semi-supervised learning framework that makes use of large text corpora and lexical resources.
Outcome:	The proposed framework improves on two benchmarks: Webquestions and Free917.

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Erel Kaplan, Tomer Bitan, Lian Ghrayeb, Le Chen, Tom Yotam, Niranjan Hasabnis, Gal Oren

Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering (2025.acl-long)

Challenge:	Parallel programming is central to modern highperformance computing, but producing parallel implementations that are correct and fast remains arduous.
Approach:	They propose a workflow that turns a Codex-based agent into an autonomous OpenMP GPU offload system . they use staged hotspot analysis, explicit data planning, correctness gating, profiling-guided refinement .
Outcome:	The proposed system outperforms a Codex-based agent on HeCBench, Rodinia, and NAS.

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Xinran Chen, Ben He, Xuanang Chen, Le Sun

DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking (2025.acl-long)

Challenge:	Open-domain question answering (ODQA) systems typically adopt a retriever-reader architecture, where the retriever finds relevant documents, and the reader extracts or synthesizes answers.
Approach:	They propose a method that iteratively adjusts the importance weights of QE terms based on their relevance, refining term distinction and enhancing the separation of relevant terms.
Outcome:	The proposed method improves retrieval accuracy and overall performance on four ODQA datasets and five QE methods.

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Zhuoqun Li, Haiyang Yu, Xuanang Chen, Hongyu Lin, Yaojie Lu, Fei Huang, Xianpei Han, Yongbin Li, Le Sun

Challenge:	Existing studies in retrieval-augmented generation (RAG) do not sufficiently address the design of complex engineering solutions.
Approach:	They propose a retrieval-augmented generation system that leverages tree-based exploration and bi-point thinking mechanism to generate reliable solutions.
Outcome:	Experiments show that the proposed system achieves state-of-the-art (SOTA) performance on the SolutionBench, highlighting its potential to enhance the automation and reliability of complex engineering solution design in real-world applications.

Contextual Interaction for Argument Post Quality Assessment (2023.emnlp-main)

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Yiran Wang, Xuanang Chen, Ben He, Le Sun

Challenge:	Existing methods for assessing the quality of natural language arguments are limited . existing methods focus on evaluating individual argument posts, but they often fail to distinguish between arguments with a narrow quality gap.
Approach:	They propose to use supervised contrastive learning to model arguments' quality . large language models with in-context examples harness the power of LLMs .
Outcome:	The proposed approach outperforms state-of-the-art models on a publicly available dataset . it shows that the LLMs with in-context examples are more effective than baseline models .

Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)

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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select (2022.emnlp-main)

Challenge:	Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader.
Approach:	They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers.
Outcome:	The proposed method improves the quality of evidence passages under zero-shot scenarios.

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Yuchen Zhuang, Yinghao Li, Junyang Zhang, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, Chao Zhang

CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection (2026.acl-long)

Challenge:	Our proposed method extracts N-ary relation tuples from scientific articles.
Approach:	They propose a method that decomposes the task into two stages . they propose modal query and modal entity selection . their results show that ReSel outperforms state-of-the-art baselines significantly .
Outcome:	The proposed method outperforms state-of-the-art baselines on three scientific information extraction datasets.

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Yihan Chen, Jiawei Chen, Guozhao Mo, Xuanang Chen, Ben He, Xianpei Han, Le Sun

Challenge:	Existing detectors rely on stylistic cues to distinguish between surface-level language refinement and genuine content generation.
Approach:	They propose a content-based detection paradigm to detect substantive AI-generation . they propose 'CoCoDet' detector that can detect surface-level language refinement .
Outcome:	The proposed detector achieves a macro F1 score of 98.24% on permissible machine-polished reviews and maintains 3.89% false positive rate on real-world reviews.

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

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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

READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)

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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Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

Challenge:	Document Structured Extraction (DSE) is a field of document structure analysis that aims to extract structured content from raw documents.
Approach:	They propose a benchmark to evaluate document structured extraction systems by converting unstructured PDFs into semantically rich Markdown.
Outcome:	The proposed benchmark is based on 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo.

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

DocQueryNet: Value Retrieval with Arbitrary Queries for Form-like Documents (2022.coling-1)

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome:	The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.

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Mingfei Gao, Le Xue, Chetan Ramaiah, Chen Xing, Ran Xu, Caiming Xiong

Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)

Challenge:	Existing methods that only address a fixed set of fields are difficult to use for different form types.
Approach:	They propose a value retrieval method with arbitrary queries for form-like documents . they propose 'docQueryNet' to predict target value based on understanding of layout and semantics of a form .
Outcome:	The proposed method outperforms existing methods on value retrieval . it improves document understanding on large-scale model pre-training by 17% .

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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching (2025.coling-main)

Challenge:	In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability.
Approach:	They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard.
Outcome:	The proposed methods significantly improve performance on six datasets.

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Tianshu Wang, Xiaoyang Chen, Hongyu Lin, Xuanang Chen, Xianpei Han, Le Sun, Hao Wang, Zhenyu Zeng

Towards Imperceptible Document Manipulations against Neural Ranking Models (2023.findings-acl)

Challenge:	Entity matching (EM) is a critical step in entity resolution (ER).
Approach:	They propose a method that incorporates record interactions from different perspectives.
Outcome:	The proposed framework improves on 8 ER datasets and 10 LLMs and achieves higher efficiency and effectiveness.

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Xuanang Chen, Ben He, Zheng Ye, Le Sun, Yingfei Sun

Robustness in NLP

Challenge:	Current approaches to detect vulnerabilities in neural ranking models often introduce noticeable errors and require a well-imitated surrogate NRM to guarantee the attack effect.
Approach:	They propose a framework called Imperceptible DocumEnt Manipulation to produce adversarial documents that are less noticeable to both algorithms and humans.
Outcome:	The proposed framework outperforms strong baselines while maintaining fluency and correctness of the target documents.

The UIR Uncertainty Corpus for Chinese: Annotating Chinese Microblog Corpus for Uncertainty Identification from Social Media (L18-1)

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Binyang Li, Jun Xiang, Le Chen, Xu Han, Xiaoyan Yu, Ruifeng Xu, Tengjiao Wang, Kam-fai Wong

Challenge:	Uncertainty identification is an important semantic processing task, critical to the quality of information in terms of factuality in many NLP techniques and applications.
Approach:	They propose to annotate Chinese microblogs with an open uncertainty corpus . they propose to use contextual uncertain semantics rather than traditional cue-phrases to identify uncertainty .
Outcome:	The proposed corpus can be used to identify uncertainty in social media texts.

CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum (2026.findings-acl)

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Tengfei Wen, Xuanang Chen, Ben He, Xiaoliang Cong, Le Sun

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation (2026.acl-long)

Challenge:	Existing methods for training data generation for low-resource languages suffer from a cold-start problem and lack diversity.
Approach:	They propose a two-stage framework that generates a high-quality, diverse, and progressively complex curriculum for Ultra Low-Resource Programming Languages (ULRPLs) they leverage the full formal syntax of the target language as structural guidance and apply a biased sampling strategy over library modules.
Outcome:	The proposed framework outperforms training-free and training-based baselines on two ULRPLs, Tengo and Janet.

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Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)

Challenge:	Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA .
Approach:	They propose a dual-LLM Questioner–Solver pipeline that integrates external knowledge from compilers and runtime feedback to generate verified translations and multi-turn dialogues.
Outcome:	The proposed model outperforms proprietary models on key metrics like compilation success and accuracy.

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Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu

Implicit Deep Latent Variable Models for Text Generation (D19-1)

Challenge:	Numerical reasoning requires both natural language understanding and arithmetic computation.
Approach:	They propose a graph representation for the context of the passage and question needed for numerical reasoning.
Outcome:	The proposed model achieves remarkable results in benchmark datasets such as DROP.

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Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, Changyou Chen

From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement (2025.emnlp-main)

Challenge:	Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons.
Approach:	They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior .
Outcome:	The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue.

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JianZhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Zike Yuan, Yang Xiang, Buzhou Tang

Challenge:	Chain-of-Thought reasoning introduces significant inference latency due to its verbosity.
Approach:	They propose a framework that leverages token elasticity phenomenon to progressively compress CoTs via multiround refinement.
Outcome:	The proposed method achieves an average accuracy improvement of 5.6% over state-of-the-art baselines while reducing CoT length by an average of 47 tokens and significantly lowering latency.

Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales (2025.findings-emnlp)

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JianZhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Yang Xiang, Buzhou Tang

Towards Self-Improving Error Diagnosis in Multi-Agent Systems (2026.findings-acl)

Challenge:	Existing work on rationale quality underestimates the importance of CoT distillation, focusing primarily on data quantity, which may result in transferring noisy or incorrect information to the student model.
Approach:	They propose a method which can discern and select high quality rationales for distillation and a Rationale Difficulty metric to measure the ability of the student model to generate the correct answer under a given rationale.
Outcome:	The proposed method achieves 4.6% accuracy improvement over baseline data on seven datasets over three tasks, controlling accuracy, diversity, and difficulty.

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Jiazheng Li, Emine Yilmaz, Bei Chen, Thu Le

Code-SPA: Style Preference Alignment to Large Language Models for Effective and Robust Code Debugging (2025.findings-acl)

Challenge:	Existing diagnostic approaches rely on expensive expert annotations and ”LLM-as-a-judge” paradigms.
Approach:	They propose a framework for semantic failure attribution that identifies responsible agents and the originating error step.
Outcome:	The proposed framework outperforms baselines in step-level localization and validation.

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Tengfei Wen, Xuanang Chen, Ben He, Le Sun

EUSP: An Easy-to-Use Semantic Parsing PlatForm (D19-3)

Challenge:	Large language models (LLMs) have demonstrated impressive capabilities in coding tasks like code generation and debugging.
Approach:	They propose a method which aligns noisy code with the well-structured style familiar to LLMs, mitigating the impact of stylistic inconsistencies.
Outcome:	The proposed method improves debugging performance on poorly styled code across the HumanEval, MBPP and EvalPlus datasets.

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Bo An, Chen Bo, Xianpei Han, Le Sun

The Source-Target Domain Mismatch Problem in Machine Translation (2021.eacl-main)

Challenge:	Semantic parsing aims to map natural language utterances into structured meaning representations.
Approach:	They propose a modular platform that allows developers to build semantic parser from scratch.
Outcome:	The proposed platform achieves competitive performance on semantic parsing task and improves performance of a business search engine.

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Jiajun Shen, Peng-Jen Chen, Matthew Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc’Aurelio Ranzato

Multilinguality

Challenge:	Despite the interconnected world we live in, people in different places talk about different things in different parts of the world.
Approach:	They propose a metric to quantify the effect of local context in machine translation and propose measurable results.
Outcome:	The proposed metric can be used to quantify the effect of local context on the use of language in machine translation systems on low resource languages.

SURE or Not? Investigating Semantic Understanding in Dense Retrieval Models (2026.acl-long)

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Lingdi Kong, Xuanang Chen, Ben He, Le Sun

Sudowoodo: A Chinese Lyric Imitation System with Source Lyrics (2023.emnlp-demo)

Challenge:	Dense retrieval models have been successful in a number of applications but it is unclear whether they truly understand semantics.
Approach:	They propose a benchmark for semantic understanding in dense retrieval that characterizes semantic precision, semantic abstraction and semantic equivalence along three dimensions.
Outcome:	The proposed model characterizes semantic understanding in dense retrieval along three dimensions: semantic precision, semantic abstraction, and semantic equivalence.

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Yongzhu Chang, Rongsheng Zhang, Lin Jiang, Qihang Chen, Le Zhang, Jiashu Pu

Analyze, Generate and Refine: Query Expansion with LLMs for Zero-Shot Open-Domain QA (2024.findings-acl)

Challenge:	Existing studies on lyrics generation focus on generating accurate lyrics using keywords, rhymes, etc. However, there is no parallel corpus for lyrics imitation.
Approach:	They propose a Chinese lyrics imitation system that can generate new lyrics based on source lyrics.
Outcome:	The proposed system can generate new lyrics based on the source lyrics . human evaluation shows it can perform better lyric imitation.

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Xinran Chen, Xuanang Chen, Ben He, Tengfei Wen, Le Sun

Low-Resource NLP

Challenge:	Existing methods like GAR and EAR rely heavily on supervised training and struggle to maintain effectiveness across domains and datasets.
Approach:	They propose a QE approach based on a three-step prompting strategy to enhance query expansion by broadening the scope of queries with additional relevant texts.
Outcome:	The proposed approach outperforms state-of-the-art methods in out-domain zero-shot scenarios and outperformed existing methods in end-to-end evaluations.

Seg2Act: Global Context-aware Action Generation for Document Logical Structuring (2024.emnlp-main)

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Zichao Li, Shaojie He, Meng Liao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Yanxiong Lu, Xianpei Han, Le Sun

Challenge:	Document logical structuring is crucial for document intelligence due to the complexity of text segment dependencies in the document.
Approach:	They propose an end-to-end, generation-based method for document logical structuring that generates the action sequence via a global context-aware generative model and updates its global context and current logical structure based on the generated actions.
Outcome:	Experiments on ChCatExt and HierDoc datasets show that Seg2Act performs better than previous methods in both supervised and transfer learning settings.

BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues (2020.emnlp-main)

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Hung Le, Doyen Sahoo, Nancy Chen, Steven C.H. Hoi

AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs (2025.naacl-long)

Challenge:	Existing approaches to video-grounded dialogues focus on superficial temporal-level visual cues, but neglect more fine-grained spatial signals from videos.
Approach:	They propose a vision-language neural framework for high-resolution queries in videos based on textual cues that exploits both spatial and temporal-level information.
Outcome:	The proposed approach outperforms previous approaches on the TGIF-QA benchmark and significantly outperformed previous approaches.

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Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Hung D Phan, Le Chen, Mihai Capotă, Theodore L. Willke, Nesreen K. Ahmed, Ali Jannesari

Accurate Text-Enhanced Knowledge Graph Representation Learning (N18-1)

Challenge:	In-Context Learning (ICL) is a powerful technique to augment the capabilities of LLMs for a diverse range of tasks.
Approach:	They propose a way to generate context using guidance from graph neural networks to generate efficient parallel codes.
Outcome:	The proposed method improves state-of-the-art LLMs by 19.9% and 6.48% on NAS and rodinia benchmarks.

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Bo An, Bo Chen, Xianpei Han, Le Sun

Understanding Differential Search Index for Text Retrieval (2023.findings-acl)

Challenge:	Existing representation learning methods for knowledge graph representation do not consider the ambiguity of relations and entities.
Approach:	They propose a text-enhanced knowledge graph representation learning method which exploits the entity descriptions and triple-specific relation mention to enhance representations.
Outcome:	The proposed method outperforms existing representation learning models on link prediction and triple classification tasks and significantly outperformed existing models.

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Xiaoyang Chen, Yanjiang Liu, Ben He, Le Sun, Yingfei Sun

GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)

Challenge:	Differentiable Search Index (DSI) is a new information retrieval framework . however, due to the black-box nature of the end-to-end neural architecture, it remains unclear to what extent it possesses basic indexing and retrieval abilities.
Approach:	They propose a multi-task distillation approach to enhance the retrieval quality without altering the structure of the model.
Outcome:	The proposed method outperforms baselines on various datasets.

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Zixiao Wang, Duzhen Zhang, Juntian Zhang, Yuhan Liu, Guoming Li, Haolun Wu, Le Song, Xiuying Chen

Adaptive Hinge Balance Loss for Document-Level Relation Extraction (2023.findings-emnlp)

Challenge:	Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary.
Approach:	They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge.
Outcome:	The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations.

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Jize Wang, Xinyi Le, Xiaodi Peng, Cailian Chen

Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs (2025.findings-acl)

Challenge:	Document-Level Relation Extraction is a multi-label classification task . however, most entity pairs do not express any relations .
Approach:	They propose to use a distance between the classification threshold and predicted score to reduce the imbalance problem by balancing the easy negatives.
Outcome:	The proposed method is superior to other methods, the authors show . it reduces the threshold to an appropriate value, and increases quadratically with the number of entities.

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Zixiao Wang, Duzhen Zhang, Ishita Agarwal, Shen Gao, Le Song, Xiuying Chen

Sparse Latents Steer Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Existing approaches to persona simulation large language models (LLMs) focus on learning basic biographical information, or using limited role-play dialogue datasets to capture a character’s responses.
Approach:	They propose to train characters using a linguistic structure and a style-tuning mechanism that allows a general linguistic style expert to collaborate with other task-specific experts to better understand their thoughts.
Outcome:	The proposed model outperforms baselines on linguistic accuracy and opinion comprehension on three tasks for Lu Xun's essay collection.

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Chunlei Xin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Xuanang Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

ReasonerRank: Redefining Language Model Evaluation with Ground-Truth-Free Ranking Frameworks (2025.findings-acl)

Challenge:	In this study, we uncover interpretable latents that govern RAG behavior in large language models . Sparse Autoencoders are used to control large language model (LLM) behavior .
Approach:	They leverage Sparse Autoencoders within the LLaMA Scope to uncover latents that govern RAG behaviors.
Outcome:	The proposed model can be used to control large language models without architectural modifications.

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Jiamu Zhang, Jiayi Yuan, Andrew Wen, Hoang Anh Duy Le, Yu-Neng Chuang, Soo-Hyun Choi, Rui Chen, Xia Hu

Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly adopted across real-world applications . traditional evaluations rely on expensive, domain-specific ground-truth labels . obtaining labeled data is expensive, time-consuming, and often requires domain expertise .
Approach:	They propose a ground-truth-free evaluation framework focused on reasoning consistency and instruction following.
Outcome:	The proposed framework outperforms existing label-free methods, including majority voting, triplet ranking, and peer-review approaches.

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Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun

Can LLMs Clarify? Investigation and Enhancement of Large Language Models on Argument Claim Optimization (2025.coling-main)

Challenge:	Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain.
Approach:	They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task .
Outcome:	The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision.

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Yiran Wang, Ben He, Xuanang Chen, Le Sun

KuiLeiXi: a Chinese Open-Ended Text Adventure Game (2021.acl-demo)

Challenge:	While Large Language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewrite, their application to claim optimization remains unexplored.
Approach:	They propose to use a sliding window mechanism to evaluate the performance of large language models in claim clarification tasks under different settings.
Outcome:	The proposed model improves the performance of three LLMs on the claim clarification task under zero-shot, few-shot and supervised fine-tuning settings.

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Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu