Papers by Weicheng Lin
TLoRA: Task-aware Low Rank Adaptation of Large Language Models (2026.acl-long)
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| Challenge: | Existing low-rank Adaptation (LoRA) methods address only one factor, often at the cost of increased training complexity or reduced practical efficiency. |
| Approach: | They propose a low-rank Adaptation framework that optimizes initialization and resource allocation at the outset of training. |
| Outcome: | The proposed framework performs excellently across various tasks while reducing the number of trainable parameters. |
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. |