Papers by Zhou Shao
EDEN: Empathetic Dialogues for English Learning (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that student passion and perseverance, or grit, is associated with language learning success. |
| Approach: | They hypothesize that as students perceive their English teachers to be more supportive, their grit improves. |
| Outcome: | The proposed chatbot improves student persistence in learning a second language. |
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)
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Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu
| Challenge: | closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges . |
| Approach: | They propose a framework that leverages collective intelligence from all large language models to evaluate each other. |
| Outcome: | a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost. |
WebInject: Prompt Injection Attack to Web Agents (2025.emnlp-main)
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| Challenge: | Despite advanced capabilities of MLLMs, prompt injection attacks remain vulnerable to emerging security and safety threats. |
| Approach: | They propose a prompt injection attack that manipulates webpage environment to induce web agent to perform attacker-specified action. |
| Outcome: | The proposed attack outperforms baselines on multiple datasets and is highly effective. |
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Experimental results show that the Reinforce-NAT system surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed. |
| Approach: | They propose a sequence-level training method and a Transformer decoder to fuse the target sequential information into the top layer of the decoded Transformer. |
| Outcome: | The proposed model surpasses the baseline NAT system on BLEU without decelerating the decoding speed and achieves comparable translation performance to the autoregressive Transformer model with considerable speedup. |
Self-Guided Alignment: Adaptive Preference Sensing for Multi-Objective Generation (2026.acl-long)
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| Challenge: | Existing approaches to align LLMs with diverse human values rely on ground-truth scores . existing approaches implicitly approximate an average-user preference, thereby failing to capture heterogeneity of human values or accommodate conflicting user needs. |
| Approach: | They propose a framework that transforms passive reward dependency into an intrinsic adaptive sensing capability. |
| Outcome: | The proposed framework outperforms state-of-the-art models in multiple model scales and improves preference alignment. |
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)
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| Challenge: | GUI automation is a key challenge in dynamic environments. |
| Approach: | They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs. |
| Outcome: | The proposed GUI-explorer shows significant improvements over existing agents. |
M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)
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Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo
| Challenge: | prevailing methods for machine translation are often hindered by misleading reward signals. |
| Approach: | They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors . |
| Outcome: | The proposed framework outperforms open-source models and achieves parity with proprietary models. |
Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)
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Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su
| Challenge: | Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches . |
| Approach: | They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap . |
| Outcome: | The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets . |
HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing approaches to extract sentence-level features are labor-intensive and time-consuming. |
| Approach: | They propose a distantly supervised relation extraction algorithm that uses circular cosine similarity to show intrinsic associations between sentences within a bag. |
| Outcome: | The proposed method outperforms baselines on the popular DSRE datasets. |
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)
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Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang
| Challenge: | Tabular data analysis is performed everyday across various domains. |
| Approach: | They propose to use a dataset of 467k tables with supervision labels for four types of field metadata. |
| Outcome: | The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information. |
Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) are now commonplace in conversation applications, but their misuse for generating harmful responses has raised serious societal concerns. |
| Approach: | They provide a comprehensive overview of recent studies covering attacks, defenses, and evaluations of Large Language Models (LLMs) . |
| Outcome: | The proposed review summarizes three aspects of LLM conversation safety: attacks, defenses, and evaluations. |
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization (2024.findings-acl)
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| Challenge: | Recent approaches to language model alignment assume homogeneous human preferences, but actual human preferences vary widely and are hard to satisfy with a single language model. |
| Approach: | They propose an RL-free extension of Direct Preference Optimization (DPO) that folds language modeling directly into reward modeling and trains language models as collective reward models that combine all objectives with specific weights. |
| Outcome: | The proposed method matches or outperforms existing methods in safety alignment and long-form question answering. |
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective (2024.acl-long)
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| Challenge: | Neural Machine Translation (NMT) has made remarkable progress over the past years, but under-translation and over-translatation remain challenging obstacles faced by NMT systems. |
| Approach: | They propose to employ the confidence of predicting the end of sentence (EOS) as a detector for under-translation and strengthen the confidence-based penalty to penalize candidates with a high risk of under-translated. |
| Outcome: | The proposed method can detect and rectify under-translated outputs, with minor impact on other correct translations. |
MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification (2025.findings-acl)
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xu Zhao Pan, Pengfei Zhou, Jiaxin Ai, Wangbo Zhao, Kai Wang, Xiaojiang Peng, Wenqi Shao, Hongxun Yao, Kaipeng Zhang
| Challenge: | Existing benchmarks of large language models focus on error detection, neglecting other scenarios like reasoning search. |
| Approach: | et al. propose a multi-task, multimodal benchmark to assess effectiveness of PRMs . step correctness, answers aggregation and reasoning process search are evaluated . ethical principles of MPBench are based on a set of evaluation paradigms based in a text-based benchmark . |
| Outcome: | a new benchmark assesses the effectiveness of large language models (LLMs) in multiple scenarios . it uses three evaluation paradigms to assess the effectiveness and compares them with existing models . a the proposed model improves reasoning accuracy by providing stepwise feedback for multi-step reasoning results . |
Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (2026.findings-acl)
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| Challenge: | Prior studies have focused on designing customized MAS for specific tasks . a critical research question remains: do LLM agent groups exhibit a form of "general intelligence" |
| Approach: | They find a Collective Intelligence factor in human groups that captures their general capability. |
| Outcome: | The proposed model predicts the ACI factor based on the features of LLM agent groups and can improve generalization abilities. |
Instruction Position Matters in Sequence Generation with Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) can perform conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning. |
| Approach: | They propose to shift the position of task instructions after the input sentences to enhance the model's instruction-following capability. |
| Outcome: | The proposed method outperforms traditional settings across various model scales (1B / 7B & 13B) and different sequence generation tasks (translation and summarization) without any additional data or annotation costs. |
InfoGain-RAG: Boosting Retrieval-Augmented Generation through Document Information Gain-based Reranking and Filtering (2025.emnlp-main)
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Zihan Wang, Zihan Liang, Zhou Shao, Yufei Ma, Huangyu Dai, Ben Chen, Lingtao Mao, Chenyi Lei, Yuqing Ding, Han Li
| Challenge: | Retrieval-Augmented Generation (RAG) frameworks struggle with identifying whether retrieved documents meaningfully contribute to answer generation. |
| Approach: | They propose a document-related metric to quantify the contribution of retrieved documents to correct answer generation. |
| Outcome: | The proposed framework outperforms existing approaches on both single and multiple retrieval paradigms. |
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have enabled dynamic reasoning in automated data analytics, but rigid, single-path workflows restrict strategic exploration and often lead to suboptimal outcomes. |
| Approach: | a new framework replaces rigid workflows with adaptive, multi-path planning . the framework offers two operating modes: SPIO-S and SPIO -E . |
| Outcome: | a new framework outperforms state-of-the-art pipelines on Kaggle and OpenML benchmarks. |
Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation (2026.acl-long)
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| Challenge: | Existing methods for learning general-purpose audio representations are limited in scope and coverage of audio attributes. |
| Approach: | They propose to use a 10.7M caption dataset to compare ALP with captioning . they find that contrastive learning yields competitive, transferable representations . |
| Outcome: | The proposed model yields competitive, transferable representations, while captioning exhibits better scalability. |
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information (2022.emnlp-main)
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| Challenge: | FormLM is a pre-trained language model for creating semi-structured forms where questions and descriptions are organized by predefined structures. |
| Approach: | They propose to enhance pre-trained language model with form structural information to model online forms and recommend form creation ideas. |
| Outcome: | The proposed model outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively. |