@inproceedings{nair-etal-2026-confidence,
title = "Confidence-Aware Ranker Ensembles for Robust In-Context Knowledge Editing",
author = "Nair, Tejal and
Nafee, Mahmud Wasif and
Jiang, Maiqi and
Gao, Ashley and
Chen, Haipeng and
Zhang, Yanfu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1750/",
pages = "35070--35080",
ISBN = "979-8-89176-395-1",
abstract = "Although large language models (LLMs) excel at factual recall, they can still propagate stale or incorrect knowledge, making in-context knowledge editing a gradient-free remedy suitable for black-box APIs. These knowledge editors that use in-context learning typically rely on a single retriever and surface-similarity heuristics to build prompts. However, a key observation in this study is that retrievers can be complementary: semantic rankers may recover paraphrased evidence, while lexical or feature-based retrievers may preserve precise entities and cues. This creates two gaps in single-retriever editors: they (i) miss complementary evidence that different retrievers surface and (ii) cannot adapt when one retriever is clearly more reliable for a query. We introduce a Feature-Weighted Ensemble for In-context Knowledge Editing (FWE-IKE) that calibrates three heterogeneous rankers (LLM-, BERT-, and MLP-based), extracts simple confidence features from each ranker, predicts per-query mixture weights, and applies a conservative margin-based routing gate that selects a single expert when confident; otherwise we mix calibrated distributions with learned per-query weights. On the CounterFact benchmark, FWE-IKE attains 88.33{\%} Edit-Success Rate, a $+3.0$ point gain over the best single retriever and approaching the oracle upper bound (91{\%}). Case studies, an ablation study, and analyses show the method systematically recovers complementary wins (e.g., BERT-only, LLM-only, MLP-only slices). FWE-IKE improves edit accuracy without touching model weights and provides a practical path to more robust, confidence-aware retrieval for IKE."
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<abstract>Although large language models (LLMs) excel at factual recall, they can still propagate stale or incorrect knowledge, making in-context knowledge editing a gradient-free remedy suitable for black-box APIs. These knowledge editors that use in-context learning typically rely on a single retriever and surface-similarity heuristics to build prompts. However, a key observation in this study is that retrievers can be complementary: semantic rankers may recover paraphrased evidence, while lexical or feature-based retrievers may preserve precise entities and cues. This creates two gaps in single-retriever editors: they (i) miss complementary evidence that different retrievers surface and (ii) cannot adapt when one retriever is clearly more reliable for a query. We introduce a Feature-Weighted Ensemble for In-context Knowledge Editing (FWE-IKE) that calibrates three heterogeneous rankers (LLM-, BERT-, and MLP-based), extracts simple confidence features from each ranker, predicts per-query mixture weights, and applies a conservative margin-based routing gate that selects a single expert when confident; otherwise we mix calibrated distributions with learned per-query weights. On the CounterFact benchmark, FWE-IKE attains 88.33% Edit-Success Rate, a +3.0 point gain over the best single retriever and approaching the oracle upper bound (91%). Case studies, an ablation study, and analyses show the method systematically recovers complementary wins (e.g., BERT-only, LLM-only, MLP-only slices). FWE-IKE improves edit accuracy without touching model weights and provides a practical path to more robust, confidence-aware retrieval for IKE.</abstract>
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%0 Conference Proceedings
%T Confidence-Aware Ranker Ensembles for Robust In-Context Knowledge Editing
%A Nair, Tejal
%A Nafee, Mahmud Wasif
%A Jiang, Maiqi
%A Gao, Ashley
%A Chen, Haipeng
%A Zhang, Yanfu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F nair-etal-2026-confidence
%X Although large language models (LLMs) excel at factual recall, they can still propagate stale or incorrect knowledge, making in-context knowledge editing a gradient-free remedy suitable for black-box APIs. These knowledge editors that use in-context learning typically rely on a single retriever and surface-similarity heuristics to build prompts. However, a key observation in this study is that retrievers can be complementary: semantic rankers may recover paraphrased evidence, while lexical or feature-based retrievers may preserve precise entities and cues. This creates two gaps in single-retriever editors: they (i) miss complementary evidence that different retrievers surface and (ii) cannot adapt when one retriever is clearly more reliable for a query. We introduce a Feature-Weighted Ensemble for In-context Knowledge Editing (FWE-IKE) that calibrates three heterogeneous rankers (LLM-, BERT-, and MLP-based), extracts simple confidence features from each ranker, predicts per-query mixture weights, and applies a conservative margin-based routing gate that selects a single expert when confident; otherwise we mix calibrated distributions with learned per-query weights. On the CounterFact benchmark, FWE-IKE attains 88.33% Edit-Success Rate, a +3.0 point gain over the best single retriever and approaching the oracle upper bound (91%). Case studies, an ablation study, and analyses show the method systematically recovers complementary wins (e.g., BERT-only, LLM-only, MLP-only slices). FWE-IKE improves edit accuracy without touching model weights and provides a practical path to more robust, confidence-aware retrieval for IKE.
%U https://aclanthology.org/2026.findings-acl.1750/
%P 35070-35080
Markdown (Informal)
[Confidence-Aware Ranker Ensembles for Robust In-Context Knowledge Editing](https://aclanthology.org/2026.findings-acl.1750/) (Nair et al., Findings 2026)
ACL