Papers with short-sighted interpretation of relevance
Why Large Language Models can Secretly Outperform Embedding Similarity in Information Retrieval (2026.acl-srw)
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| Challenge: | Recent studies show that similarity is a short-sighted interpretation of relevance . LLM-based Relevance Judgment Systems (LLM-RJS) can overcome this limitation . |
| Approach: | They propose that LLM-Based Relevance Judgment Systems can overcome short-sighted interpretation of relevance by embedding similarity instead of similarity. |
| Outcome: | The proposed methods outperform Neural Embedding Retrieval Systems by overcoming similarity limitation. |