Papers by Jian Shao
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models (2025.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs . however, it is hindered by two critical challenges: substantial GPU memory consumption and low activated parameters. |
| Approach: | They propose an Expert-Selection Aware Compressor for Mixture-of-Experts (MoE) that aligns with the characteristics of MoE from the perspectives of quantization and pruning. |
| Outcome: | The proposed approach significantly reduces memory usage and improves inference speed with minimal performance degradation. |
SEEK: Segmented Embedding of Knowledge Graphs (2020.acl-main)
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| Challenge: | Existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them far from satisfactory. |
| Approach: | They propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. |
| Outcome: | The proposed framework can achieve highly competitive relational expressiveness without increasing model complexity. |
Natural Language Video Localization with Learnable Moment Proposals (2021.emnlp-main)
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| Challenge: | Existing methods for video moment localization have poor performance due to predefined rules. |
| Approach: | They propose a model with a fixed set of learnable moment proposals with 'border-aware loss' they propose to localize the video moment corresponding to the query by locating the start and end timestamps in an untrimmed video. |
| Outcome: | The proposed model outperforms state-of-the-art models on two challenging benchmarks. |
A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. |
| Approach: | They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs . |
| Outcome: | The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities . |
Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling (2026.acl-long)
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Qiyuan Chen, Hongsen Huang, Jiahe Chen, Qian Shao, Jintai Chen, Hongxia Xu, Renjie Hua, Ren Chuan, Jian Wu
| Challenge: | Existing multimodal reward models are interpretable but slow, while discriminative ones are opaque "black boxes." |
| Approach: | They propose a framework that dynamically decomposes evaluation into granular, interpretable dimensions. |
| Outcome: | The proposed framework outperforms open-source reward models on benchmarks like VL-RewardBench. |
Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang
| Challenge: | Large language models (LLMs) have shown remarkable performance, but their training costs are exorbitant. |
| Approach: | They propose a parameter-efficient method for exploring optimal solutions within latent space by using latent units to extract input representations from LLMs. |
| Outcome: | The proposed method improves performance on a range of natural language processing tasks. |
Icon2: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation (2025.emnlp-main)
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Qiyuan Chen, Hongsen Huang, Qian Shao, Jiahe Chen, Jintai Chen, Hongxia Xu, Renjie Hua, Ren Chuan, Jian Wu
| Challenge: | Large Language Models (LLMs) require high quality preference datasets to align with human preferences. |
| Approach: | They propose a framework that leverages inherent regulation of LLMs’ representation space for efficient and tailored preference dataset construction, named Icon2. |
| Outcome: | The proposed framework improves performance on benchmarks like AlpacaEval 2.0 and Arena-Hard while reducing computational costs by up to 48.1%. |
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering (2024.emnlp-main)
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| Challenge: | Recent advances with LLMs have shown promising results across various tasks, but their use in answering questions from knowledge bases remains largely unexplored. |
| Approach: | They propose a framework that utilizes an LLM-based agent with multiple roles for KBQA tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks yielding F1 scores of 11.8% and 20.7%, respectively. |