FGR-ColBERT: Identifying Fine-Grained Relevance Tokens During Retrieval
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918421446262784 |
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| author | Jarolím, Antonín Fajčík, Martin |
| author_facet | Jarolím, Antonín Fajčík, Martin |
| contents | Document retrieval identifies relevant documents but does not provide fine-grained evidence cues, such as specific relevant spans. A possible solution is to apply an LLM after retrieval; however, this introduces significant computational overhead and limits practical deployment. We propose FGR-ColBERT, a modification of ColBERT retrieval model that integrates fine-grained relevance signals distilled from an LLM directly into the retrieval function. Experiments on MS MARCO show that FGR-ColBERT (110M) achieves a token-level F1 of 64.5, exceeding the 62.8 of Gemma 2 (27B), despite being approximately 245 times smaller. At the same time, it preserves retrieval effectiveness (99% relative Recall@50) and remains efficient, incurring only a ~1.12x latency overhead compared to the original ColBERT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00242 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FGR-ColBERT: Identifying Fine-Grained Relevance Tokens During Retrieval Jarolím, Antonín Fajčík, Martin Information Retrieval Computation and Language Document retrieval identifies relevant documents but does not provide fine-grained evidence cues, such as specific relevant spans. A possible solution is to apply an LLM after retrieval; however, this introduces significant computational overhead and limits practical deployment. We propose FGR-ColBERT, a modification of ColBERT retrieval model that integrates fine-grained relevance signals distilled from an LLM directly into the retrieval function. Experiments on MS MARCO show that FGR-ColBERT (110M) achieves a token-level F1 of 64.5, exceeding the 62.8 of Gemma 2 (27B), despite being approximately 245 times smaller. At the same time, it preserves retrieval effectiveness (99% relative Recall@50) and remains efficient, incurring only a ~1.12x latency overhead compared to the original ColBERT. |
| title | FGR-ColBERT: Identifying Fine-Grained Relevance Tokens During Retrieval |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2604.00242 |