ChunkRank: Model-Aware Text Chunking and Abstention-Aware Answer Selection for LLM Pipelines
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Computer Science > Computation and Language
arXiv:2609.29828 (cs)
[Submitted on 24 Sep 2026]
Title:ChunkRank: Model-Aware Text Chunking and Abstention-Aware Answer Selection for LLM Pipelines
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Abstract:We present ChunkRank, an open-source Python library that derives chunk boundaries from a target model's tokenizer and context window, and selects an answer among candidates produced independently per chunk. It ships a validated registry of 90 models across 15 providers and six answer-selection methods, and needs only three core dependencies. For chunking, ChunkRank avoids context-window overflow automatically from the model name, whereas character-based splitters overflow or waste the budget, and a fidelity study across 11 languages shows why token-exact budgets matter beyond English. For answer selection we report a negative result: on NaturalQuestions, TriviaQA and HotpotQA, with extractive and generative readers, no content-based ranker reliably beats taking the first non-empty answer. The reason is reader abstention on chunks that lack the answer, not answer position. A long-context baseline shows that chunking matches single-call reading on single-hop questions, so ChunkRank targets small-window and beyond-window settings. Code, registry and evaluation harness are released.
| Comments: | 16 pages. Code: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2609.29828 [cs.CL] |
| (or arXiv:2609.29828v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29828
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ChunkRank: Model-Aware Text Chunking and Abstention-Aware Answer Selection for LLM Pipelines, by Amit Nautiyal and 2 other authors
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