Bold Claims or Self-Doubt? Factuality Hallucination Type Detection via Belief State (2025)

Abstract: Large language models are prone to generating hallucination that deviates from factual information. Existing studies mainly focus on detecting the presence of hallucinations but lack a systematic classification approach, which hinders deeper exploration of their characteristics. To address this, we introduce the concept of belief state, which quantifies the model’s confidence in its own responses. We define the belief state of the model based on self-consistency, leveraging answer repetition rates to label confident and uncertain states. Based on this, we categorize factuality hallucination into two types: Overconfident Hallucination and Unaware Hallucination. Furthermore, we propose BAFH , a factuality hallucination type detection method. By training a classifier on model’s hidden states, we establish a link between hidden states and belief states, enabling efficient and automatic hallucination type detection. Experimental results demonstrate the effectiveness of BAFH and the differences between hallucination types.

## introduction large language models (llms) have demonstrated remarkable capabilities in various natural language processing tasks @xcite . however, there's a concerning trend where they exhibit an inclination to generate hallucination @xcite @xcite , which makes it risky to deploy llms in practical scenarios. consequently, accurately detecting and addressing hallucination has become a significant research challenge @xcite . existing llm hallucination detection methods mainly focus on identifying factual errors in llm outputs, which are commonly referred to as factuality hallucination @xcite , 2023; @xcite . for instance, @xcite utilize external tools for evidence gathering to detect factual errors. if the model's output does not align with evidence, it is considered a potential hallucination @xcite @xcite . another category of methods do not rely on external knowledge, but instead detect hallucination by estimating the uncertainty of model outputs @xcite @xcite . for example, mind @xcite leverages the hidden states of llms for real-time hallucination detection without requiring manual annotations. despite significant progress in factuality hallucination detection, existing work still has notable limitations. current research primarily focuses on detecting the presence of factuality hallucination, with insufficient attention given to the detailed analysis of specific types of hallucination. a few studies @xcite ) that have attempted to classify hallucination typically base on semantic errors (e.g., factual or logical errors), but they lack a general classification framework and automated methods. these limitations constrain deeper understanding of the hallucination mechanisms in llms. therefore, we focus on factuality hallucination and pose the critical questions: "can factuality hallucination be categorized into distinct types? how can we effectively differentiate between these types of hallucination?" existing studies have shown that models exhibit a degree of self-awareness @xcite . building on these insights, we observe that models also display different levels of uncertainty regarding the hallucinations they produce. we describe this uncertainty as the model's belief state and propose a belief-state-based classification paradigm for factuality hallucination, as illustrated in figure 1 . we define belief states by measuring the consistency across different responses. as analyzed and discussed in section 3, we categorize them into two types: confident state and uncertain state. hallucinations generated by the model in confident belief state are referred to as overconfident hallucinations, while those generated in uncertain belief state are termed unaware hallucinations. given these considerations, we developed belief-state-aware factuality hallucination type detection (bafh) method, a lightweight framework that integrates with transformer-based llms. bafh leverages hidden states to determine belief states and classify hallucination types. in summary, our contributions are as follows: hallucination classification in early studies, hallucinations are broadly categorized into intrinsic and extrinsic hallucinations based on whether the correctness of the output could be verified against the source content @xcite @xcite . recent research has expanded these classifications to encompass hallucinations in broader contexts. for example, considering the user-centered interaction emphasized by llms, @xcite classify hallucination into factuality and faithful hallucination. faithful hallucination reflects the logical consistency within the generated content @xcite . factuality hallucination refers to outputs containing factual inaccuracies that can be verified against reliable sources. while existing frameworks provide valuable insights @xcite , their classification basis is often limited to task-specific or semantic levels, making them inadequate for comprehensively describing the complex generative behaviors of llms. to this end, we propose a new classification criteria and a detection method for factuality hallucination types and conduct comparative analysis of the characteristics of different hallucination types. ## motivation in this section, we analyze the repetition count of model responses. @xcite point out that the self-consistency of model responses reflects the model's uncertainty, which we refer to as belief states. specifically, we define belief states as the model's internal confidence level in its generated responses, which can be inferred indirectly through the consistency of repeated outputs. to figure 3: statistical analysis of model response consistency for gemma-2-9b-it on nqopen. figure 3 shows that as repetition count increases, correct responses become more frequent, indicating that a higher repetition count is generally linked to greater confidence and accuracy. moreover, while most factual errors have a low repetition count, some still occur with high repetition. this suggests that llms may retain high confidence even when generating hallucinations, implying that not all hallucinations stem from uncertainty. notably, response repetition counts exhibit an uneven distribution, with higher counts (e.g., 10) and lower counts (e.g., 1-5) being more common, while intermediate counts (e.g., 6-8) are relatively rare. this pattern may reflect a form of metacognitive behavior of llm, suggesting that it tends to be either confident (frequent repetition) or uncertain (minimal repetition), rather than distributing its confidence uniformly across responses (more details are provided in appendix a.1). based on these observations, we hypothesize that this distribution may reflect a clustering of the model's belief state around two primary modes, which we refer to as confident state and uncertain state. correspondingly, we categorize the hallucinations arising from these states as overconfident hallucinations and unaware hallucinations. section 4 presents our hallucination type detection method. ## results we conduct experiments to evaluate our proposed factuality hallucination type detection method. specifically, this section aims to answer the following research questions (rqs): rq1: does bafh achieve good performance? rq2: how do the two types of hallucinations differ from each other and from correct answers? rq3: can hidden state of llms be used to distinguish different types of hallucinations? ## conclusion we propose a belief-state-based factuality hallucination classification method and introduce bafh, a hallucination type detection method. experimental results show that bafh achieves high accuracy across multiple datasets. furthermore, different types of hallucinations are distinct in the distribution of hidden states, and llms exhibit distinct behavioral patterns when encountering different hallucination types. however, llms struggle to recognize the hallucination types of their own. in summary, our research reveals distinctions among factuality hallucination categories and highlights the significance of hallucination classification.