Papers by Guangyao Shen
Towards Tool Use Alignment of Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on tool use with LLMs focus on enhancing tool-calling ability of LLM . e.g., LLM should not answer unsafe tool use relevant instructions or insecure tool responses to ensure reliability and harmlessness. |
| Approach: | They propose to use supervised fine-tuning and preference learning to align LLMs with H2A principle for tool use. |
| Outcome: | The proposed model demonstrates that LLMs can generate truthful and helpful responses while remaining harmless. |
CURE: Critique-Driven Unified Reinforcement Learning for Test-Time Self-Improvement (2026.acl-long)
Copied to clipboard
| Challenge: | Existing critique-guided methods fail to equip models with the autonomous improvement capabilities required for test-time scaling. |
| Approach: | They propose a framework that jointly optimizes a single policy for standard solving, critiquing, and guided re-exploration. |
| Outcome: | The proposed framework maintains competitive single-turn performance and unlocks effective inference-time scaling. |