analiza aiqbench
The paper presents a retrieval benchmark for Indian Knowledge Systems, with a fixed BM25 baseline that scores weakly on verse retrieval and on assembling complete evidence for multi-hop questions. It is useful for readers tracking how retrieval for AI and language systems performs on low-resource, culturally specific corpora. The baseline is classical rather than neural, so the main contribution is the evaluation resource, not a learned model.
Dh\={a}r\={a} Bench: An Evidence-Oriented Retrieval Benchmark for Indian Knowledge Systems
arXiv:2611.03196v1 Announce Type: cross Abstract: Dh\={a}r\={a} Bench is a retrieval benchmark for Indian Knowledge Systems (IKS), with 325 questions in 12 categories and 60,121 text chunks from 15 source groups. The categories were developed based on 4,500 questions submitted to the Dh\={a}r\={a} application; the evaluation set combines corpus-aligned application queries with expert-contributed questions. The annotations comprise 7,578 question-chunk relevance links involving 6,847 distinct corpus chunks. For 34 composite and multi-hop questions, 401 conjugate groups specify alternative sets of passages that jointly support each question. Chunk identifiers and hierarchical references connect the judgments to their source texts. The paper describes corpus preparation, annotation, and validation, and reports a fixed BM25 baseline implemented with Pyserini with $k_1=0.9$ and $b=0.4$. Across all questions, BM25 achieves MRR@20 of 0.315, Recall@10 of 0.182, and nDCG@10 of 0.205. On the group-annotated subset, complete-evidence scores are CE_Recall@20 = 0.294 and CE_MRR@20 = 0.141. The category results expose particularly weak verse retrieval, while group scores reveal gaps between finding individual passages and completing their support. The corpus and evaluation code are available at https://github.com/Avinash2236/Dhara-Bench.
Abstract:Dhārā Bench is a retrieval benchmark for Indian Knowledge Systems (IKS), with 325 questions in 12 categories and 60,121 text chunks from 15 source groups. The categories were developed based on 4,500 questions submitted to the Dhārā application; the evaluation set combines corpus-aligned application queries with expert-contributed questions. The annotations comprise 7,578 question-chunk relevance links involving 6,847 distinct corpus chunks. For 34 composite and multi-hop questions, 401 conjugate groups specify alternative sets of passages that jointly support each question. Chunk identifiers and hierarchical references connect the judgments to their source texts. The paper describes corpus preparation, annotation, and validation, and reports a fixed BM25 baseline implemented with Pyserini with $k_1=0.9$ and $b=0.4$. Across all questions, BM25 achieves MRR@20 of 0.315, Recall@10 of 0.182, and nDCG@10 of 0.205. On the group-annotated subset, complete-evidence scores are CE_Recall@20 = 0.294 and CE_MRR@20 = 0.141. The category results expose particularly weak verse retrieval, while group scores reveal gaps between finding individual passages and completing their support. The corpus and evaluation code are available at this https URL.
18 pages, 6 figures, 9 tables. The first two authors contributed equally. Code and data: this https URL
Information Retrieval (cs.IR); Computation and Language (cs.CL)
arXiv:2611.03196 [cs.IR]
(or arXiv:2611.03196v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2611.03196 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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