@inproceedings{cheng-etal-2026-probing,
title = "Probing Benchmark-Specific Regularities in Knowledge Gating via Lightweight Feature Analysis",
author = "Cheng, Zilong and
Chen, Yubo and
Sun, Jingbo and
Gao, Ge and
Chen, Bo and
Zhao, Xiaobing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.913/",
pages = "19303--19311",
ISBN = "979-8-89176-462-0",
abstract = "We study whether knowledge-gating benchmarks support transferable retrieval decisions or expose benchmark-specific structural regularities that can be exploited without full semantic interpretation. Knowledge gating is the binary decision of whether a dialogue system should retrieve external knowledge at a given turn. We audit this question with a lightweight logistic-regression probe over ten dialogue-metadata features: position, length, and question-form cues. Despite using only metadata and surface signals, the probe reveals non-trivial in-domain signal and markedly different structural patterns across benchmark families. Cross-benchmark transfer provides the clearest evidence: transfer within the related DSTC9/DSTC11 family is near-lossless (Macro F1 0.81{--}0.84; ROC-AUC {\ensuremath{\approx}} 0.90), whereas transfer across the KETOD{--}DSTC benchmark-family boundary has weak ranking quality (ROC-AUC 0.41{--}0.48) and poor detection of knowledge-needed turns (positive-class F1 {\ensuremath{\leq}} 0.22 in one direction). Sentence embeddings and fine-tuned BERT show the same qualitative weakness in the DSTC{\textrightarrow}KETOD setting: MiniLM ranking is sub-chance (ROC-AUC 0.40{--}0.44), while BERT remains near chance (0.50{--}0.53) despite having the strongest KETOD in-domain positive F1. These results suggest that strong in-domain gating scores can partly reflect benchmark-specific regularities and should not, by themselves, be interpreted as evidence of transferable retrieval reasoning. Code and reproducibility materials are available at https://github.com/zilongcheng582-cyber/RAGate-analysis."
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<abstract>We study whether knowledge-gating benchmarks support transferable retrieval decisions or expose benchmark-specific structural regularities that can be exploited without full semantic interpretation. Knowledge gating is the binary decision of whether a dialogue system should retrieve external knowledge at a given turn. We audit this question with a lightweight logistic-regression probe over ten dialogue-metadata features: position, length, and question-form cues. Despite using only metadata and surface signals, the probe reveals non-trivial in-domain signal and markedly different structural patterns across benchmark families. Cross-benchmark transfer provides the clearest evidence: transfer within the related DSTC9/DSTC11 family is near-lossless (Macro F1 0.81–0.84; ROC-AUC \ensuremath\approx 0.90), whereas transfer across the KETOD–DSTC benchmark-family boundary has weak ranking quality (ROC-AUC 0.41–0.48) and poor detection of knowledge-needed turns (positive-class F1 \ensuremathłeq 0.22 in one direction). Sentence embeddings and fine-tuned BERT show the same qualitative weakness in the DSTC→KETOD setting: MiniLM ranking is sub-chance (ROC-AUC 0.40–0.44), while BERT remains near chance (0.50–0.53) despite having the strongest KETOD in-domain positive F1. These results suggest that strong in-domain gating scores can partly reflect benchmark-specific regularities and should not, by themselves, be interpreted as evidence of transferable retrieval reasoning. Code and reproducibility materials are available at https://github.com/zilongcheng582-cyber/RAGate-analysis.</abstract>
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%0 Conference Proceedings
%T Probing Benchmark-Specific Regularities in Knowledge Gating via Lightweight Feature Analysis
%A Cheng, Zilong
%A Chen, Yubo
%A Sun, Jingbo
%A Gao, Ge
%A Chen, Bo
%A Zhao, Xiaobing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cheng-etal-2026-probing
%X We study whether knowledge-gating benchmarks support transferable retrieval decisions or expose benchmark-specific structural regularities that can be exploited without full semantic interpretation. Knowledge gating is the binary decision of whether a dialogue system should retrieve external knowledge at a given turn. We audit this question with a lightweight logistic-regression probe over ten dialogue-metadata features: position, length, and question-form cues. Despite using only metadata and surface signals, the probe reveals non-trivial in-domain signal and markedly different structural patterns across benchmark families. Cross-benchmark transfer provides the clearest evidence: transfer within the related DSTC9/DSTC11 family is near-lossless (Macro F1 0.81–0.84; ROC-AUC \ensuremath\approx 0.90), whereas transfer across the KETOD–DSTC benchmark-family boundary has weak ranking quality (ROC-AUC 0.41–0.48) and poor detection of knowledge-needed turns (positive-class F1 \ensuremathłeq 0.22 in one direction). Sentence embeddings and fine-tuned BERT show the same qualitative weakness in the DSTC→KETOD setting: MiniLM ranking is sub-chance (ROC-AUC 0.40–0.44), while BERT remains near chance (0.50–0.53) despite having the strongest KETOD in-domain positive F1. These results suggest that strong in-domain gating scores can partly reflect benchmark-specific regularities and should not, by themselves, be interpreted as evidence of transferable retrieval reasoning. Code and reproducibility materials are available at https://github.com/zilongcheng582-cyber/RAGate-analysis.
%U https://aclanthology.org/2026.findings-emnlp.913/
%P 19303-19311
Markdown (Informal)
[Probing Benchmark-Specific Regularities in Knowledge Gating via Lightweight Feature Analysis](https://aclanthology.org/2026.findings-emnlp.913/) (Cheng et al., Findings 2026)
ACL