Do LVLM s Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLM s (2025)

Abstract: Large Vision-Language Models (LVLMs) demonstrate strong visual question answering (VQA) capabilities but are shown to hallucinate. A reliable model should perceive its knowledge boundaries—knowing what it knows and what it does not. This paper investigates LVLMs’ perception of their knowledge boundaries by evaluating three types of confidence signals: probabilistic confidence, answer consistency-based confidence, and verbalized confidence. Experiments on three LVLMs across three VQA datasets show that, although LVLMs possess a reasonable perception level, there is substantial room for improvement. Among the three confidence, probabilistic and consistency-based signals are more reliable indicators, while verbalized confidence often leads to overconfidence. To enhance LVLMs’ perception, we adapt several established confidence calibration methods from Large Language Models (LLMs) and propose three effective methods. Additionally, we compare LVLMs with their LLM counterparts, finding that jointly processing visual and textual inputs decreases question-answering performance but reduces confidence, resulting in improved perception level compared to LLMs.

## introduction large vision-language models (lvlms) are capable of processing both textual and visual information simultaneously, demonstrating strong performance on visual question-answering (vqa) task @xcite @xcite . however, when faced with questions beyond their knowledge boundaries, lvlms often hallucinate-generating seemingly plausible but factually incorrect responses @xcite . this is unacceptable in safetycritical domains such as healthcare. knowing when an lvlm can answer correctly not only helps us determine when to trust the model but also enables adaptive retrieval-augmented generation (rag), triggering rag only when the model does not know the answer, which improves both the efficiency and effectiveness of rag @xcite . a trustworthy model should have a clear perception of its knowledge boundaries-knowing what it knows and what it does not. while this ability has been extensively studied in large language models (llms) @xcite @xcite , it remains underexplored in lvlms. a model's perception level is assessed by the alignment between its confidence and actual performance, with correctness of the answer serving as a proxy for performance. therefore, the emphasis is on whether lvlms can provide confidence that matches their performance. we focus on binary confidence because it directly helps us decide whether to trust the model. in this work, we explore this question by examining three representative types of confidence signals that are widely used in llms: 1) probabilistic confidence @xcite . the confidence is measured by the generation probability of tokens in the output. we conduct experiments using three representative models--qwen2.5-vl @xcite , deepseek-vl2 @xcite , and llava-v1.5 @xcite )-on three datasets which fo-cus on different dimensions of model capabilities: dyn-vqa @xcite , mmmu pro @xcite , and visual7w @xcite . the results show that lvlms can perceive their knowledge boundaries to some extent but they are far from perfect. among the three types of confidence, probabilistic and answer consistency-based confidences are more aligned with lvlms' performance but rely on in-domain data for binarization, while verbalized confidence has weaker alignment and tends to be overconfident. to enhance lvlms' perception capabilities, we adopt representative confidence calibration methods originally developed for llms. results show that prompting-based approaches, which engage the model's chain-of-thought process, can improve both answer accuracy and verbalized perception, whereas consistency-based methods are less effective and fail to generalize to lvlms. moreover, we propose three lvlm-oriented approaches: img-cot, which converts visual information into textual descriptions before reasoning; prob-thr, which derives binary confidences by thresholding predicted probabilities; and cross model consistency, which measures reliability by comparing responses across different models. compared to llms, lvlms need to process an additional visual modality and integrate information across different modalities. this raises the question: how does the visual understanding functionality in lvlms impact its knowledge boundary perception? to investigate this, we compare the lvlms with their corresponding llms on the dyn-vqa dataset @xcite . this dataset provides parallel visual-textual and pure textual queries, ensuring fair comparison between llms and lvlms. we focus on verbalized confidence because it can reflect the model's language capabilities. experimental results show that: 1) lvlms exhibit lower vqa performance but better perception capability alignment compared to their llm counterparts. 2) prompt-based perception enhancement methods shown to be effective on llms do not always work well on lvlms, indicating that lvlms have weaker instruction-following capabilities. these phenomena may be caused by the following two reasons: 1) compared with single-modality inputs, processing visual information and integrating multiple modalities is more challenging for lvlms. this leads to lower vqa performance but also reduces overconfidence, resulting in improved perception. 2) when model capacity is insufficient to accommodate additional visual information, language abilities may be compromised, weakening instruction-following skills. controlled experiments across different model scales and input modalities support these reasons. lvlms. previous studies have established the widespread adoption of lvlms in safety-critical domains such as healthcare @xcite and autonomous driving @xcite . while these applications demonstrate lvlms' functional capabilities, studies show lvlms frequently produce hallucinations @xcite . the current body of work investigates this limitation on different aspects. some work surveys hallucination types and their causes @xcite @xcite , while others focus on mitigating hallucinations @xcite @xcite . a distinct but less explored research thread investigates lvlms' knowledge boundary as a potential framework for enhancing model reliability @xcite @xcite . we take this line of work a step further by introducing a novel comparative paradigm that compares perception between lvlms with their llm counterparts. ## preliminary in this section, we provide an overview of our task. ## knowledge boundary perception in lvlms this section presents the experimental framework for evaluating lvlms' knowledge boundary perception capabilities, including confidence elicita-tion methods, confidence calibration techniques, and quantitative assessment of perception levels. ## overall performance ## further analysis compared to llms, lvlms need to process additional visual modality and integrate information across different modalities. this raises a question: how does the perception of lvlms differ from that of llms? knowing these distinctions is valuable for developing trustworthy lvlms. in this section, we investigate the difference of knowledge boundary perception between lvlms and their llm counterparts. focusing on verbalized confidence cause it directly reflects models' self-awareness of their knowledge boundaries. we further propose several hypotheses about these differences' underlying causes and validate them through the comparison between different model scales and input modalities. ## conclusion in this paper, we present a systematic investigation of knowledge boundary perception in lvlms, assessing this ability through alignment. first, we evaluate three kinds of confidence, and observe that answer consistency-based confidence reaches the highest alignment, whereas verbalized confidence induces overconfidence. we also evaluate several confidence calibration methods, with our results revealing that reasoning elicitation methods improve accuracy and alignment, while our proposed methods show effectiveness. second, we compare lvlms with llms, and reveal that while lvlms exhibit lower qa accuracy, they achieve higher alignment, which is attributable to lvlms' awareness of their multimodal integration ability limitation. we also observe that lvlms have weaker instruction following ability than llms.