Papers by Liye Zhao
Personalizing LLMs with Binary Feedback: A Preference-Calibrated Optimization Framework (2026.acl-long)
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| Challenge: | Existing methods focus on isolated user histories, neglecting the essential role of inter-user differences. |
| Approach: | They propose a framework that personalizes Large Language Models via preference-calibrated binary signals. |
| Outcome: | The proposed framework outperforms baselines in a variety of personalization tasks and backbone LLMs. |
E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning (2026.findings-acl)
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| Challenge: | Existing training paradigms for Large Language Models (LLMs) suffer from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT-then-RL is limited by high data costs and capability plateaus caused by low-entropy collapse. |
| Approach: | They propose an Enhanced Experience Exploitation paradigm that integrates expert prefixes, expert guided, and self-exploration to improve agent training. |
| Outcome: | The proposed model achieves a 6% performance improvement over traditional paradigms on tool-use tasks while requiring less than 10% of the synthetic data. |