BITEM 参加 NTCIR-19 R2C2 任务：从 Agentic RAG 流水线信号预测置信度

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Abstract:The BITEM team entered both subtasks of the NTCIR-19 R2C2 task with a single agentic pipeline, in which a model searches, reads and records evidence over a movie corpus while an orchestrator holds the record and rules on what may be submitted. A claim is admitted only once an entailment cascade has checked it against the passage it cites, and an answer is released only once enough checked evidence stands behind it. Each question is run three or four times, every pass retrieving from a corpus stripped of what the earlier passes have already seen. The confidence score predicted with each answer is computed by the orchestrator from what the run leaves behind and is never asked of the model, which is offered no way to rate itself. The two retrieval runs placed 4th and 5th of 22, pooling the passes was worth 0.0709 nDCG@20, and the gain was largest on the multi-hop and post-processing-heavy questions, where the organisers rank the pooled run top of the field. Sixteen of the 25 answer runs were built on passages these two runs supplied, 12 of them submitted by other teams. HMR rewards a system whose confidence is high where it answers right and low where it answers wrong. The pipeline reached an accuracy of 0.9219, 6th of 25, while the confidence scores predicted for those answers gave an HMR of 0.4915, 13th. A few rules crafted over those same recorded signals, with no further model call and no further retrieval, raise that to an accuracy of 0.9375, 5th, and an HMR of 0.6985, 9th. Ranking on HMR alone can reward a system for answering wrongly with low confidence, so we propose accHMR, the accuracy multiplied by HMR, which reports the reward in proportion to the accuracy, and on which the revised rules would have scored 0.6549, 5th. For future work, fitting a model on the numbers the pipeline already produces, rather than writing such rules by hand, would be a real step forward.

Comments:	8 pages. Participant paper for the NTCIR-19 R2C2 task
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
ACM classes:	H.3.3; I.2.7
Cite as:	arXiv:2609.37993 [cs.CL]
	(or arXiv:2609.37993v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2609.37993 arXiv-issued DOI via DataCite

Submission history

From: Julien Knafou [view email]
[v1] Tue, 29 Sep 2026 16:52:38 UTC (90 KB)
[v2] Thu, 29 Oct 2026 16:45:07 UTC (85 KB)