Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA (2025)

Abstract: Large language models (LLMs) encode vast amounts of world knowledge but remain static once trained, making timely integration of emerging facts prohibitively expensive via full retraining. Knowledge-editing techniques have thus emerged to inject or overwrite specific facts into LLMs, yet they either over-rely on superficial cues or incur complex, iterative pipelines that collapse under noisy, multi-hop conditions. We introduce **Reason-KE**, an end-to-end reasoning-chain-based editing framework that steers a pretrained LLM through four structured stages—fact acknowledgment, relevance determination, selective application, and final reasoning—to filter distractors in a single pass. Trained on MQuAKE-CF with up to four irrelevant facts, Reason-KE elevates Qwen2.5-7B’s multi-hop QA accuracy to 90.2% (↑17.6 pp) while suffering merely 6.3% drop under heavy distraction and <1% when answers are leaked. Our quantitative analysis confirms Reason-KE’s resilience and efficiency, establishing a new state of the art for reliable LLM knowledge updates. The code will be released.

## introduction large language models (llms, @xcite @xcite have shown strong capabilities in natural language understanding, generation, and reasoning @xcite @xcite . however, these models encode world knowledge statistically, and updating emerging facts via full retraining is prohibitively expensive. to address this limitation, knowledge editing (ke) techniques @xcite @xcite @xcite have been proposed to inject or overwrite the specific facts in pretrained llms without retraining from scratch. existing ke methods can be categorized into two main groups. parameter modification approaches @xcite directly alter model weights to integrate new information, while parameter preservation methods @xcite @xcite add lightweight modules or leverage in-context learning to achieve editing with minimal changes to the base model. although parameter-preservation frameworks perform well on multi-hop question answering (mqa) benchmarks @xcite , they often rely too heavily on surface-level context cues. as a result, their performance degrades sharply when faced with noisy or irrelevant facts, a scenario common in real-world applications @xcite . to overcome these challenges, we introduce reason-ke, an end-to-end reasoning-chain-based ke framework that guides a pretrained llm through four structured stages: 1) acknowledgment of updated information; 2) relevance determination to filter distractors; 3) selective application of pertinent facts; and 4) final reasoning to derive the answer. by explicitly modeling each reasoning step in a single pass 1 , reason-ke eliminates the need for complex iterative pipelines and maintains robustness under heavy distraction (figure 1 ). ## experimental setup baselines and models. we compare our method with various model editing methods, including the parameter modification method rome @xcite and parameter preservation methods mello @xcite , pokemqa @xcite , editcot @xcite , and rae @xcite . for mello and pokemqa, we employ deepseek-v3 @xcite foot_1 as the backbone models, and the other are conducted on qwen2.5-instruct-7b @xcite . in addition to the qwen model, we also validate the effectiveness of our method in llama3-8b-instruct @xcite . more details are provided in appendix b.1. ## main rsults we demonstrate the result of our method and baselines in reason-ke outperforms other methods in multihop setting by a significant margin. as shown in reason-ke brings stable improvements on more complex editing sets e. the more facts that need editing, the more interference terms added and the more complex the editing set e becomes. this setting demands that the model not only comprehends the facts in e but also exhibits robust reasoning capabilities to arrive at the final answer. as demonstrated in ## analysis reason-ke works well for other models and datasets. to validate the generalization of our method, we conducted analyses on other models and datasets. as shown in does every composition of the training data matter? we focus on the importance of the reasoning process in reason-ke. as shown in ## conclusion we present reason-ke, a streamlined end-to-end framework that guides an llm through four explicit reasoning stages-acknowledgment, relevance filtering, selective application, and final inference-to perform multi-hop knowledge edits in one pass. on mquake-cf-3k, reason-ke boosts qwen2.5-7b's accuracy to 90.2% (↑17.6 pp) while limiting drops to 6.3% with four distractors and under 1% when answers leak. ablations confirm that each reasoning stage is critical to robustness, and inference is markedly faster than iterative baselines. reason-ke offers a simple, reliable, and efficient solution for updating llm world knowledge.