Papers with ExPerT
ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation (2025.findings-acl)
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| Challenge: | Evaluating personalized text generated by large language models is challenging, as only the LLM user, i.e. prompt author, can reliably assess the output. |
| Approach: | They propose an explainable reference-based evaluation framework that leverages an LLM to extract atomic aspects and their evidences from the generated and reference texts, match the aspects, and evaluate their alignment based on content and writing style. |
| Outcome: | The proposed framework achieves a 7.2% improvement in alignment with human judgments compared to the state-of-the-art evaluation methods. |
ExPerT: Personalizing LLM Responses to Users’ Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues (2026.acl-long)
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| Challenge: | Existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. |
| Approach: | They propose a query-wise personalization framework that adapts LLM responses to query domain expertise by combining semantic and behavioral cues. |
| Outcome: | ExPerT reduces expertise inference error by 65.7% compared to the strongest baseline and improves response satisfaction by 17.52% on a 5-point Likert scale. |