Papers by Xiaofeng Zhou
Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement (2025.findings-acl)
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| Challenge: | Existing methods for enhancing small models struggle to yield substantial and lasting performance gains. |
| Approach: | They propose a Debate and Reflect framework that orchestrates multi-turn debates between smaller models and stronger teacher models. |
| Outcome: | The proposed framework outperforms existing methods by a large margin in smaller models. |
VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding (2022.findings-emnlp)
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| Challenge: | Pre-trained language models have been widely applied to standard benchmarks due to the limited resources available in a domain. |
| Approach: | They propose a Transformer-based language model called VarMAE for domain-adaptive language understanding that encodes the context of a token into a smooth latent distribution. |
| Outcome: | Experiments on science- and finance-domain NLU tasks show that the proposed model can be efficiently adapted to new domains with limited resources. |
Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
MirrorQA: Benchmarking Multimodal LLMs on Mirror-Orientation Reasoning (2026.acl-long)
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| Challenge: | Multimodal large language models (MLLMs) have achieved remarkable progress in recent years, yet their ability to perform left–right reasoning in mirror contexts remains underexplored. |
| Approach: | They propose a benchmark to evaluate MLLMs' ability to distinguish left from right from a subject-centered perspective. |
| Outcome: | The proposed benchmarks show that even the best performing models achieve only 65.40% accuracy, far below the 99.28% accuracy of humans. |
ShieldHead: Decoding-time Safeguard for Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in LLM-based moderation methods have demonstrated remarkable promise in identifying safety risks associated with both inputs and outputs in human-AI interactions. |
| Approach: | They propose to learn a classification head on the last-layer hidden states of a dialogue model and use it to detect harmful content. |
| Outcome: | The proposed framework is 300 faster (**1ms**) than previous LLM-based moderation models with 99% less parameters than LlamaGuard. |
Mixture of Attention Heads: Selecting Attention Heads Per Token (2022.emnlp-main)
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| Challenge: | Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. |
| Approach: | They propose a new architecture that combines multi-head attention with the MoE mechanism and a sparsely gated architecture that allows for faster computations. |
| Outcome: | The proposed architecture can scale up the number of attention heads and the number parameters while preserving computational efficiency. |
MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning (2025.coling-main)
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| Challenge: | LoRA is a key technique for fine-tuning large pre-trained models, yet its performance in multi-task learning scenarios often falls short. |
| Approach: | They propose a mixture-of-shared-LoRAs model with a dropout strategy . they propose to share the upper projection matrix among different experts . |
| Outcome: | The proposed model exhibits excellent performance in both single-task and multi-task scenarios with robust out-of-domain generalization capabilities. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)
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Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou
| Challenge: | Large Language Models (LLMs) can expand their capabilities by integrating external tools. |
| Approach: | They propose a training framework that prepares LLMs for diverse generalization challenges in tool utilization. |
| Outcome: | The proposed framework improves the tool-usage capabilities of LLMs by up to 8B parameters, surpassing GPT-4o. |
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings (P19-1)
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| Challenge: | Lexical relations are relations between terms in lexicons. |
| Approach: | They propose a neural representation learning model to distinguish lexical relations among term pairs based on hyperspherical relation embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks. |