Papers by Weiwei Yang
Context-DPO: Aligning Language Models for Context-Faithfulness (2025.findings-acl)
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Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang, Zihan Zhang, Haizhen Huang, Lingrui Mei, Junfeng Fang, Zehao Li, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Shenghua Liu
| Challenge: | Context-DPO is the first alignment method specifically designed to enhance contextfaithfulness for large language models. |
| Approach: | They propose a benchmark that simulates Retrieval-Augmented Generation scenarios with knowledge conflicts to evaluate context-faithfulness. |
| Outcome: | The proposed method improves LLMs' context-faithfulness by 35% to 280% over open-source models. |
MAIN: Mutual Alignment Is Necessary for instruction tuning (2025.emnlp-main)
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Fanyi Yang, Jianfeng Liu, Xin Zhang, Haoyu Liu, Xixin Cao, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Instruction tuning has enabled large language models to achieve remarkable performance, yet its success heavily depends on the availability of high-quality instruction-response pairs. |
| Approach: | They propose a mutual alignment framework which enforces coherence between instructions and responses through mutual constraints. |
| Outcome: | The proposed framework generalizes well across model architectures and sizes, achieving state-of-the-art performance on LLaMA, Mistral, and Qwen models across diverse benchmarks. |
FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension (2024.emnlp-main)
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| Challenge: | Referring Expression Comprehension (REC) is a cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding. |
| Approach: | They propose to use a new reference expression comprehension (REC) dataset to evaluate the capabilities of language understanding, image comprehension, and language-to-image grounding. |
| Outcome: | The proposed model is able to reject scenarios where the target object is not visible in the image, a key aspect often overlooked in existing models and approaches. |
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition (2024.acl-long)
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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Large language models (LLMs) are a promising alternative to expensive human evaluations. |
| Approach: | They propose a framework that iteratively aligns LLM-based evaluators with human preference . they decompose a given evaluation task into finer-grained criteria . |
| Outcome: | The proposed framework iteratively aligns LLM-based evaluators with human preference . it decomposes a given evaluation task into finer-grained criteria . the framework is efficient to train and more explainable than relying solely on prompts . |
FlowHN: Adaptive Token Routing for Efficient Parallel Hybrid Networks (2026.acl-industry)
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| Challenge: | Existing hybrids lack performance, latency, and cost-efficient scaling for production LLMs. |
| Approach: | They propose a deployment-oriented parallel hybrid architecture that enables deterministic conditional computation via FLOP-aware token circulation across attention and SSM branches. |
| Outcome: | FlowHN achieves 4 higher throughput and 15% higher MFU than current models while maintaining competitive accuracy on reasoning, coding, and long-context tasks. |
Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities (2025.naacl-long)
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Chung-En Sun, Xiaodong Liu, Weiwei Yang, Tsui-Wei Weng, Hao Cheng, Aidan San, Michel Galley, Jianfeng Gao
| Challenge: | Recent research shows that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks. |
| Approach: | They propose a framework that crafts adversarial LLMs with enhanced jailbreak ability. |
| Outcome: | ADV-LLM significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. |
CrowdSelect: SyntheticInstruction Data Selection with Multi-LLM Wisdom (2026.findings-eacl)
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| Challenge: | Existing methods for capturing instruction-following complexity rely on single-dimensional signals, but they fail to capture complexity across diverse fields. |
| Approach: | They propose three foundational metrics that leverage Multi-LLMs wisdom to capture instruction-response pair characteristics and propose CrowdSelect, an integrated metric incorporating a clustering-based approach to maintain response diversity. |
| Outcome: | The proposed metrics outperform existing models on MT-bench and Arena-hard and show improvements of 4.81% on full and LoRA fine-tuning. |
Auto Search Indexer for End-to-End Document Retrieval (2023.findings-emnlp)
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| Challenge: | Generative retrieval heavily relies on the “preprocessed” document identifiers, thus limiting its retrieval performance and ability to retrieve new documents. |
| Approach: | They propose a fully end-to-end retrieval paradigm that can learn the best docids for existing and new documents automatically via a semantic indexing module. |
| Outcome: | The proposed model outperforms baselines on public and industrial datasets and can handle new documents. |
ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)
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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang
| Challenge: | In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks. |
| Approach: | They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance . |
| Outcome: | The proposed method improves query-ad relevance labeling performance by providing demonstrations. |
FLOP-Efficient Training: Early Stopping Based on Test-Time Compute Awareness (2026.findings-acl)
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| Challenge: | Prior work shows that increasing test-time compute (TTC) can improve accuracy of large language models. |
| Approach: | They propose a TTC-aware training algorithm that jointly selects a checkpoint and a corresponding TTC configuration to minimize training compute without sacrificing accuracy. |
| Outcome: | The proposed method reduces training compute by 92% while maintaining accuracy. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)
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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun
| Challenge: | Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities. |
| Approach: | They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning. |
| Outcome: | The proposed approach outperforms existing LLMs on an open-source and industrial dataset. |
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech (D19-3)
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| Challenge: | Language model adaptation (LMA) is a promising solution for conversational speech recognition systems. |
| Approach: | They propose to use language model adaptation techniques to adapt language models to conversational speech recognition. |
| Outcome: | The proposed toolkit compares state-of-the-art language model adaptation techniques in conversational speech recognition tasks. |
A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability (D19-1)
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| Challenge: | Existing models implicitly assume that documents in different languages are highly comparable, a false assumption. |
| Approach: | They propose a multilingual topic model that learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar. |
| Outcome: | The proposed model outperforms existing models in low-resource language tasks and outperformed LDA and previous models in classification tasks using documents’ topic posteriors as features. |
Calibrating LLM-Based Evaluator (2024.lrec-main)
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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Existing models for large language models lack the ability to calibrate their outputs towards human preference. |
| Approach: | They propose a multi-stage, gradient-free approach to calibrate an LLM-based evaluator toward human preference. |
| Outcome: | The proposed approach improves correlation with expert evaluation on multiple text quality evaluation datasets. |