Papers by Hui Zhao

LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection (2024.naacl-short)

Challenge:	Recent studies have fine-tuned judge models based on open-source LLMs to evaluate the quality of other LLM.
Approach:	They propose to use open-source LLMs to evaluate Large Language Models (LLMs) their empirical results show that the models underperform GPT-4 in several dimensions .
Outcome:	The proposed models outperform GPT-4 on several dimensions including generalizability, fairness and adaptability.

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Zhao Zhang, Yiming Li, Jin Zhang, Hui Xu

Sentiment Analysis

Challenge:	Existing methods for stance detection focus on background information and not on the accompanying input texts.
Approach:	They propose to prompt Large Language Models to explicitly extract the relationship between paired text and unseen target as contextual knowledge and inject it into a generation model BART to exploit the rich contexts and semantics.
Outcome:	The proposed model is able to detect stance labels in zero-shot and cross-target scenarios.

InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)

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Kaixin Li, Qisheng Hu, James Zhao, Hui Chen, Yuxi Xie, Tiedong Liu, Michael Shieh, Junxian He

Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models (2026.findings-acl)

Challenge:	InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing.
Approach:	They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing .
Outcome:	The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark .

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Zhining Liu, Tianyi Wang, Xiao Lin, Penghao Ouyang, Gaotang Li, Ze Yang, Hui Liu, Sumit Keswani, Vishwa Pardeshi, Huijun Zhao, Wei Fan, Hanghang Tong

VisiPruner: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs (2025.emnlp-main)

Challenge:	Vision-Language Models (VLMs) have advanced multimodal learning, driving progress in cross-modal reasoning.
Approach:	They propose to examine moral robustness of vision-language models by analyzing their moral stances under multimodal perturbations.
Outcome:	The proposed model-agnostic multimodal perturbations expose VLMs to a variety of moral vulnerabilities, including a sycophancy trade-off where stronger instruction-following models are more susceptible to persuasion.

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Yingqi Fan, Anhao Zhao, Jinlan Fu, Junlong Tong, Hui Su, Yijie Pan, Wei Zhang, Xiaoyu Shen

ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) suffer from significant computational overhead due to the quadratic growth of attention computations with the number of multimodal tokens.
Approach:	They propose a training-free pruning framework that prunes multimodal tokens without a trained pruning method.
Outcome:	The proposed pruning framework outperforms existing token pruning methods and generalizes across diverse MLLMs.

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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin

Rumor Detection on Social Media with Crowd Intelligence and ChatGPT-Assisted Networks (2023.emnlp-main)

Challenge:	Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0.
Approach:	They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes.
Outcome:	The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation.

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Chang Yang, Peng Zhang, Wenbo Qiao, Hui Gao, Jiaming Zhao

ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information (2024.findings-emnlp)

Challenge:	Existing research on rumor detection challenges the expressive power of text encoding sequences, and insufficient mining of semantic structural information.
Approach:	They propose a Crowd Intelligence-based semantic feature learning module to capture textual content’s sequential and hierarchical features and a knowledge-based structural mining module that leverages ChatGPT for knowledge enhancement.
Outcome:	The proposed system achieves performance improvement in rumor detection tasks validating the effectiveness and rationality of using large language models as auxiliary tools.

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Zheng Hui, Zhaoxiao Guo, Hang Zhao, Juanyong Duan, Congrui Huang

Responsible & Trustworthy NLP

Challenge:	Existing models for detecting harmful content lack diversity and quality of datasets.
Approach:	They propose a framework for synthesizing toxic information from social media datasets . their framework generates a wide variety of synthetic, yet remarkably realistic, examples of toxic information .
Outcome:	The proposed framework can generate a wide variety of synthetic, yet remarkably realistic, examples of toxic information.

Ruleformer: Context-aware Rule Mining over Knowledge Graph (2022.coling-1)

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Zezhong Xu, Peng Ye, Hui Chen, Meng Zhao, Huajun Chen, Wen Zhang

SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction (2020.acl-main)

Challenge:	Existing work on rule mining focuses on mining rules, but how to select appropriate rules for completion of different triplets has not been discussed.
Approach:	They propose to take context information into consideration when selecting suitable rules . they devise a transformer-based rule mining approach, Ruleformer .
Outcome:	The proposed model takes context information into consideration, which helps select suitable rules for inference tasks.

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He Zhao, Longtao Huang, Rong Zhang, Quan Lu, Hui Xue

Sentiment Analysis

Challenge:	Aspect terms and opinion terms are key problems of fine-grained aspect-based sentiment analysis.
Approach:	They propose a method to extract aspect and opinion terms as pairs from a sentence . they use shared spans to extract the terms under supervision of span boundaries .
Outcome:	The proposed method outperforms state-of-the-art methods on both aspects and opinion terms extraction tasks.

Two Challenges, One Solution: Robust Multimodal Learning through Dynamic Modality Recognition and Enhancement (2025.findings-emnlp)

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Lanxin Bi, Yunqi Zhang, Luyi Wang, Yake Niu, Hui Zhao

Robustness in NLP

Challenge:	Existing methods require full-modality data during training phase or require explicit annotations to detect missing modalities.
Approach:	They propose a Dynamic modality Recognition and Enhancement for Adaptive Multimodal fusion framework that directs selective reconstruction of missing or underperforming modalities.
Outcome:	The proposed framework outperforms several baseline and state-of-the-art models on three benchmark datasets.

DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task (2023.findings-emnlp)

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Guanting Dong, Tingfeng Hui, Zhuoma GongQue, Jinxu Zhao, Daichi Guo, Gang Zhao, Keqing He, Weiran Xu

Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks (2024.naacl-long)

Challenge:	Recent prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks.
Approach:	They propose a multi-task demonstration-based generative framework for noisy slot filling that captures input perturbations at different granularities.
Outcome:	The proposed framework outperforms baseline methods and achieves strong generalization.

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Yunqi Zhang, Songda Li, Chunyuan Deng, Luyi Wang, Hui Zhao

Visual Data in NLP

Challenge:	Existing vision-language models focus on salient attributes but ignore contextualized nuances, resulting in gender bias.
Approach:	They propose a task-agnostic generation framework to mitigate gender bias in vision-language models.
Outcome:	The proposed framework can mitigate gender bias in vision-language models . it yields all-sided but gender-obfuscated narratives, which prevents concentration on localized image features, especially gender attributes.

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay (2024.emnlp-main)

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Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, Hao Wang

Challenge:	Existing studies on LLM agents' social behaviors are lacking . previous studies focused on positive social behaviors, leaving research on negative social behaviors relatively scarce.
Approach:	They propose a framework that features a multi-agent system facilitating efficient communication and interaction with LLM agents.
Outcome:	The proposed framework is based on Avalon and evaluates on game success and analyzes agents’ social behaviors.

Mitigating Hallucinations in Multi-modal Large Language Models via Image Token Attention-Guided Decoding (2025.naacl-long)

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Xinhao Xu, Hui Chen, Mengyao Lyu, Sicheng Zhao, Yizhe Xiong, Zijia Lin, Jungong Han, Guiguang Ding

MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)

Challenge:	Multi-modal large language models (MLLMs) generate plausible but incorrect content, resulting in hallucinations . recent advances in MLLM technology have demonstrated their outstanding performance in a variety of visual tasks, such as object detection.
Approach:	They propose a plug-and-play method which leverages MLLMs’ internal representations to mitigate hallucinations by analyzing input and output tokens.
Outcome:	The proposed method exploits MLLMs’ internal representations to mitigate hallucinations.

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Hui Huang, Jiaheng Liu, Yancheng He, Shilong Li, Bing Xu, Conghui Zhu, Muyun Yang, Tiejun Zhao

Iterative Nearest Neighbour Machine Translation for Unsupervised Domain Adaptation (2023.findings-acl)

Challenge:	Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application.
Approach:	They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model.
Outcome:	The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity.

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Hui Huang, Shuangzhi Wu, Xinnian Liang, Zefan Zhou, Muyun Yang, Tiejun Zhao

Low-Resource NLP

Challenge:	Existing methods for supervised domain adaptation of machine translation focus on fine-tuning, which is non-extensible.
Approach:	They propose to perform unsupervised domain adaptation in a non-parametric manner by using in-domain monolingual data and performing nearest neighbour inference on both forward and backward directions.
Outcome:	The proposed method significantly improves the in-domain translation performance and achieves state-of-the-art results among non-parametric methods.

ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition (2026.findings-acl)

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Jiaming Zhou, Yujie Guo, Shiwan Zhao, Yao Lu, Jianye Wang, Haoqin Sun, Hui Wang, Yong Qin

Dialogue Systems & Conversational Agents

Challenge:	Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora.
Approach:	They propose a large-scale Chinese child speech corpus that contains 112.5 hours of speech from 498 children and 500 caregivers.
Outcome:	The proposed model improves in-domain and cross-domain performance on children's speech.

Probing and Boosting Large Language Models Capabilities via Attention Heads (2025.emnlp-main)

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Dezhi Zhao, Xin Liu, Xiaocheng Feng, Hui Wang, Bing Qin

Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator (D19-1)

Challenge:	Existing approaches to identifying capabilities rely on external signals with limited structural grounding . emergence of specific capabilities remains poorly understood .
Approach:	They propose a lightweight approach that links LLM capabilities to internal components by identifying correspondences at the level of attention heads.
Outcome:	The proposed approach improves accuracy on MMLU and BBH by 1 to 1.5 points over gradient-based method and 5 to 6 points over other intermediate-state baselines.

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Xiaoyu Shen, Yang Zhao, Hui Su, Dietrich Klakow

Text Generation

Challenge:	Modern pointer generators only capture exact word matches, ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment.
Approach:	They propose a pointer generator architecture that allows the model to "edit" pointed tokens instead of always copying them.
Outcome:	The proposed model captures more latent alignment relations than exact word matches and generates higher-quality summaries validated by both qualitative and quantitative evaluations.

Diversifying Dialogue Generation with Non-Conversational Text (2020.acl-main)

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Hui Su, Xiaoyu Shen, Sanqiang Zhao, Zhou Xiao, Pengwei Hu, Randy Zhong, Cheng Niu, Jie Zhou

Dialogue Systems & Conversational Agents

Challenge:	Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation.
Approach:	They propose a way to diversify dialogue generation by leveraging non-conversational text . they collect large-scale corpus from forum comments, idioms and book snippets .
Outcome:	The proposed model produces significantly more diverse responses without sacrificing relevance with context.

LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)

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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng

Reasoning

Challenge:	E-commerce search relevance is a critical component of retrieval systems.
Approach:	They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies.
Outcome:	The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain.

AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation (2026.findings-acl)

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Wentao Shi, Yu Wang, Yuyang Zhao, Yuxin Chen, Fuli Feng, Xueyuan Hao, Xi Su, Qi GU, Hui Su, Xunliang Cai, Xiangnan He

Challenge:	Existing approaches to verify agent behaviors in complex environments rely on rule-based verifiers or LLM-as-a-Judge models.
Approach:	They propose a benchmark to evaluate Agent-as-a-Judge across three domains . the benchmark covers search, data systems, and graphical user interfaces - with 155 tasks and 516 trajectories .
Outcome:	The proposed benchmark outperforms existing benchmarks in search, data systems, and GUI domains while revealing open challenges in agent-based verification.

CSL: A Large-scale Chinese Scientific Literature Dataset (2022.coling-1)

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Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, Hui Zhang

Information Extraction & Text Mining

Challenge:	Existing datasets centered around the English language restrict development of Chinese scientific NLP.
Approach:	They present a large-scale Chinese scientific literature dataset based on Chinese papers . they use semi-structured data as a natural annotation for many supervised NLP tasks .
Outcome:	The proposed dataset can serve as a Chinese corpus and perform many supervised tasks.

Improving Translation Quality Estimation with Bias Mitigation (2023.acl-long)

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Hui Huang, Shuangzhi Wu, Kehai Chen, Hui Di, Muyun Yang, Tiejun Zhao

Multilinguality

Challenge:	State-of-the-art translation Quality Estimation models are biased, relying on monolingual features while ignoring the bilingual semantic alignment.
Approach:	They propose a method to mitigate the bias of translation quality estimation models by contrastive learning between clean and noisy sentence pairs.
Outcome:	The proposed method improves the estimation performance while mitigating the bias.

UER: An Open-Source Toolkit for Pre-training Models (D19-3)

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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du

Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem (2024.lrec-main)

Challenge:	Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently.
Approach:	They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules.
Outcome:	The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored.

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YuHong Sun, Zhangyue Yin, Qipeng Guo, Jiawen Wu, Xipeng Qiu, Hui Zhao

Reasoning

Challenge:	Large language models (LLMs) are highly effective in various natural language processing tasks, but can produce unreliable conjectures in ambiguous contexts, which is known as hallucination.
Approach:	They propose a method to evaluate LLM hallucination in Question Answering based on the unanswerable math word problem (UMWP) . they combine text similarity and mathematical expression detection to determine whether LLM considers the question unanswered.
Outcome:	The proposed method combines text similarity and mathematical expression detection to determine whether the LLM considers the question unanswerable.

EmotionTalk: An Interactive Chinese Multimodal Emotion Dataset With Rich Annotations (2026.findings-acl)

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Haoqin Sun, Jinghua Zhao, Xuechen Wang, Shiwan Zhao, Jiaming Zhou, Hui Wang, Xi Yang, Yequan Wang, Yonghua Lin

MPVStance: Mitigating Hallucinations in Stance Detection with Multi-Perspective Verification (2025.acl-long)

Challenge:	Existing datasets face issues such as low quality, limited scale, and incomplete modalities, hindering model performance.
Approach:	They propose to use Chinese multimodal datasets to capture authentic emotional interplay from 19 professional actors.
Outcome:	The EmotionTalk dataset spans 23.6 hours of dyadic conversations across diverse scenarios.

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ZhaoDan Zhang, Zhao Zhang, Jin Zhang, Hui Xu, Xueqi Cheng

Sentiment Analysis

Challenge:	despite advances in large language models, challenges persist due to hallucination-models generating inaccurate content.
Approach:	They propose a framework that integrates multi-perspective verification with Retrieval-Augmented Generation to address these challenges.
Outcome:	The proposed method outperforms existing models on the SemEval-2016 and VAST datasets.

Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)

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Suhuang Wu, Huimin Wang, Yutian Zhao, Xian Wu, Yefeng Zheng, Wei Li, Hui Li, Rongrong Ji

Responsible & Trustworthy NLP

Challenge:	Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails.
Approach:	They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks.
Outcome:	The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses.

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

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

AdaTP: Attention-Debiased Token Pruning for Video Large Language Models (2025.findings-emnlp)

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

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Fengyuan Sun, Leqi Shen, Hui Chen, Sicheng Zhao, Jungong Han, Guiguang Ding

Self-Evaluation of Large Language Model based on Glass-box Features (2024.findings-emnlp)

Challenge:	Existing visual token compression methods rely on attention scores but have inherent biases . global and local attention biased scores cause excessive computational overhead .
Approach:	They propose a token pruning pipeline that targets global and local attention biases . the pipeline is designed to reduce computational overhead of Video Large Language Models based on visual tokens compiled from multiple video frames .
Outcome:	The proposed method significantly reduces the computational overhead of Video Large Language Models while retaining the performance of vanilla models.

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Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu

FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction (2024.lrec-main)

Challenge:	Existing evaluation methods rely on external evaluators, focusing on training and prompting strategies, but model-aware glass-box features are overlooked.
Approach:	They propose to use model-aware glass-box features to evaluate an LLM's output.
Outcome:	The proposed model-aware features are reliable quality indicators for self-evaluation on public benchmarks.

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Xudong Zhu, Zhao Kang, Bei Hui

Information Extraction & Text Mining

Challenge:	Document-level Relation Extraction (DocRE) aims to identify relation labels between entities within a document.
Approach:	They propose to fuse constituency and dependency syntax into DocRE to exploit the rich syntax information in the document.
Outcome:	The proposed method is able to identify relation labels between entities within a document and is scalable.

SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs (2026.acl-long)

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Yanxiao Zhao, Yaqian Li, Zi-Hao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, null Renlei, Xiaolin Qin, Kaiwen Long

Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)

Challenge:	Large language models exhibit strong general reasoning abilities, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve them.
Approach:	They propose a verifier that generates diverse SAT-based reasoning tasks from CNF instances and checks answers objectively with PySAT.
Outcome:	The proposed verifier generates diverse SAT-based reasoning tasks from CNF instances and checks answers objectively with PySAT.

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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang

Structured Data in NLP

Challenge:	Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities.
Approach:	They propose a taxonomy spanning Graph-Assisted Knowledge Augmentation, Graph Assisted Reasoning and Planning, and Graphed LLM Collaboration.
Outcome:	The proposed models show that graphs can augment and correct LLMs and support dynamic coordination among experts and agents in collaborative settings.

Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment (2024.lrec-main)

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Yingxiu Zhao, Bowen Yu, Binyuan Hui, Haiyang Yu, Minghao Li, Fei Huang, Nevin L. Zhang, Yongbin Li

Challenge:	Extensive research has highlighted the importance of data complexity as a crucial metric, but the impact of complexity remains relatively unexplored.
Approach:	They propose to add a specified number of nodes to instructions’ semantic trees to enhance the instruction complexity in a controllable manner.
Outcome:	The proposed approach outperforms diverse yet complex instructions under the same token budget and can control the difficulty level of modified instructions.

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)

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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang

Programming Languages in NLP

Challenge:	Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows .
Approach:	They propose a repository-level evaluation benchmark to assess security of AI-generated code.
Outcome:	The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation.

Better Late Than Never: Model-Agnostic Hallucination Post-Processing Framework Towards Clinical Text Summarization (2024.findings-acl)

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Songda Li, Yunqi Zhang, Chunyuan Deng, Yake Niu, Hui Zhao

Text Generation

Challenge:	Existing methods for generating concise and coherent summaries may include unintended text with hallucinations, causing computational costs.
Approach:	They propose a model-agnostic framework to post-process medical hallucinations . MEDAL integrates with any medical summarization model, requiring no additional computational overhead .
Outcome:	MEDAL can post-process medical hallucinations without additional computational overhead.

SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)

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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin