Papers by Hilaf Hasson
HyQE: Ranking Contexts with Hypothetical Query Embeddings (2024.findings-emnlp)
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| Challenge: | Existing approaches to rank contexts rely on similarity between contexts and queries, but these methods are limited by the number of candidate contexts. |
| Approach: | They propose a scalable ranking framework that combines embedding similarity and large language models without fine-tuning. |
| Outcome: | The proposed framework improves the performance across multiple benchmarks. |
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning (2026.acl-long)
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Xiang Gao, Yuguang Yao, Qi Zhang, Kaiwen Dong, Avinash Baidya, Ruocheng Guo, Hilaf Hasson, Kamalika Das
| Challenge: | Large language models (LLMs) struggle to use tools reliably in domain-specific settings. |
| Approach: | They propose a neuro-symbolic approach to adapt large language models to task-specific tools . they propose reusable rules that are distilled from failure traces and injected into the prompt . |
| Outcome: | Experiments show that the proposed approach outperforms prompting-based adaptation methods and complements finetuning. |