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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.26205 |
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| _version_ | 1866915525114724352 |
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| author | Mangold, Aline Hoffmann, Kiran |
| author_facet | Mangold, Aline Hoffmann, Kiran |
| contents | Retrieval-augmented generation (RAG) systems are increasingly deployed in user-facing applications, yet systematic, human-centered evaluation of their outputs remains underexplored. Building on Gienapp's utility-dimension framework, we designed a human-centred questionnaire that assesses RAG outputs across 12 dimensions. We iteratively refined the questionnaire through several rounds of ratings on a set of query-output pairs and semantic discussions. Ultimately, we incorporated feedback from both a human rater and a human-LLM pair. Results indicate that while large language models (LLMs) reliably focus on metric descriptions and scale labels, they exhibit weaknesses in detecting textual format variations. Humans struggled to focus strictly on metric descriptions and labels. LLM ratings and explanations were viewed as a helpful support, but numeric LLM and human ratings lacked agreement. The final questionnaire extends the initial framework by focusing on user intent, text structuring, and information verifiability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human-Centered Evaluation of RAG outputs: a framework and questionnaire for human-AI collaboration Mangold, Aline Hoffmann, Kiran Artificial Intelligence Retrieval-augmented generation (RAG) systems are increasingly deployed in user-facing applications, yet systematic, human-centered evaluation of their outputs remains underexplored. Building on Gienapp's utility-dimension framework, we designed a human-centred questionnaire that assesses RAG outputs across 12 dimensions. We iteratively refined the questionnaire through several rounds of ratings on a set of query-output pairs and semantic discussions. Ultimately, we incorporated feedback from both a human rater and a human-LLM pair. Results indicate that while large language models (LLMs) reliably focus on metric descriptions and scale labels, they exhibit weaknesses in detecting textual format variations. Humans struggled to focus strictly on metric descriptions and labels. LLM ratings and explanations were viewed as a helpful support, but numeric LLM and human ratings lacked agreement. The final questionnaire extends the initial framework by focusing on user intent, text structuring, and information verifiability. |
| title | Human-Centered Evaluation of RAG outputs: a framework and questionnaire for human-AI collaboration |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.26205 |