Papers by Hongjiang Li
MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks. |
| Approach: | They propose a framework that optimizes MAS prompts as a maximum a posteriori problem and then iteratively updates agent prompts. |
| Outcome: | The proposed framework surpasses manual and automated benchmarks in multiple tasks and provides general guidelines for building more reliable and principled multi-agent systems in the future. |
PsyMem: Fine-grained Psychological Alignment and Explicit Memory Control for Advanced Role-Playing LLMs (2026.tacl-1)
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| Challenge: | Existing role-playing models rely on superficial textual descriptions or simplistic metrics, inadequately modeling both intrinsic and extrinsic character dimensions. |
| Approach: | They propose a framework that integrates fine-grained psychological attributes and explicit memory control for role-playing. |
| Outcome: | The proposed framework outperforms baseline models in human-likeness and character fidelity. |
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)
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| Challenge: | Existing models focus on aspect term extraction, opinion term extraction and sentiment polarity classification but ignore the difference. |
| Approach: | They propose a joint aspect-based sentiment analysis task that focuses on the difference between the two tasks to improve the model's robustness. |
| Outcome: | Empirical results show that the proposed model outperforms the previous state-of-the-art on four benchmark datasets. |