Whether LLM s Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning (2025)

Abstract: In active retrieval (AR), large language models (LLMs) need first assess whether they possess knowledge to answer a given query, to decide whether to invoke a retrieval module. Existing methods primarily rely on training classification models or using the confidence of the model’s answer to determine knowledge boundaries. However, training-based methods may have limited generalization, and our analysis reveals that LLMs struggle to reliably assess whether they possess the required information based on their answers, often biased by prior cognitive tendencies (e.g., tokens’ semantic preferences). To address this, we propose Debiased Historical In-Context Learning (DH-ICL) to identify knowledge boundaries in AR. DH-ICL aims to reframe this self-awareness metacognitive task as a structured pattern-learning problem by retrieving similar historical queries as high-confidence in-context examples to guide LLMs to identify knowledge boundaries. Furthermore, we introduce a historical bias calibration strategy that leverages deviations in the model’s past response logits to mitigate cognitive biases in its current knowledge boundary assessment. Experiments on four QA benchmarks show that DH-ICL achieves performance comparable to full retrieval on LLaMA with only half the number of retrievals, without any additional training.

## introduction in open-domain question answering @xcite and knowledge-intensive tasks @xcite , active retrieval (ar) @xcite enhances the accuracy and efficiency of retrieval by having large language models (llms) @xcite @xcite first assess whether they possess sufficient knowledge to answer a query before deciding whether to invoke an external retrieval * corresponding author. ## related work different from passive retrieval, which applies retrieval for every query in rag methods @xcite @xcite , active retrieval (ar) (gosselin and @xcite aims to selectively retrieve external knowledge only when necessary. ar reduces unnecessary retrieval overhead and prevents potential interference from irrelevant or low-quality retrieved information. some recent studies train lightweight classifiers to predict whether retrieval is needed based on input queries @xcite @xcite @xcite . @xcite @xcite . for instance, @xcite triggers retrieval only when the model exhibits high uncertainty in its predictions. @xcite evaluates selfaware uncertainty by computing the determinant of the regularized gram matrix of hidden representations from multiple sampled generations. @xcite assesses whether the model already knows the answer by explicitly prompting the llm to output confidence scores. a key challenge in active retrieval is enabling llms to recognize their knowledge boundaries @xcite @xcite , ensuring efficient retrieval by minimizing redundancy while enhancing knowledge augmentation when necessary. despite progress made by approaches based on training classification models and leveraging model confidence scores, these methods often suffer from limited generalization ability and deployment efficiency across different datasets and llms. our experiments further reveal that llms generally struggle with self-assessing their knowledge boundaries and exhibit prior cognitive biases. to this end, we propose dh-icl to improve knowledge boundary recognition. ## conclusion in this work, we introduced debiased historical in-context learning (dh-icl) to enhance the reliability of knowledge boundary identification in active retrieval. rather than relying on direct self-assessment, which llms inherently struggle with, dh-icl reframes the problem as a structured pattern-learning task, leveraging historical highconfidence examples to guide retrieval decisions. additionally, we proposed a historical bias calibration strategy, which adjusts logits deviations from historical responses to mitigate biases in current knowledge boundary identification. extensive experiments across multiple qa benchmarks demonstrated that dh-icl effectively reduces unnecessary retrievals while maintaining strong performance. notably, our method is both efficient and generalizable, requiring no additional training. as users continue to interact with the llm system, the historical experience log is continuously updated, leading to increasingly accurate knowledge boundary judgments over time. furthermore, cold-start analysis shows that dh-icl retains its effectiveness even in the absence of historical logs.