H allu T ree: Explainable Multi-Hop Hallucination Detection for Abstractive Summarization (2025)

Abstract: Black-box verifiers for abstractive summaries often struggle with complex claims that require multi-hop reasoning, and they typically provide a single verdict without an interpretable rationale. As a result, it becomes difficult to understand or audit their failures. We address this with HalluTree, a framework that models verification as an interpretable claim tree. HalluTree first decomposes summaries into subclaims, classifying each into two types – extractive (directly verifiable against evidence) or inferential (requiring reasoning) – which follow distinct verification paths. Extractive claims are robustly verified against evidence using an ensemble of lightweight NLI models. Crucially, inferential claims trigger a process that generates a natural program – an explicit reasoning chain that integrates supporting evidence and logical steps – which is then executed to determine the claim’s validity. Evaluation on the LLM-AggreFact benchmark demonstrates HalluTree’s effectiveness: it achieves performance competitive with top-tier black-box models, including Bespoke-MiniCheck, while providing transparent and auditable reasoning programs for every inferential judgment. This combination of competitive accuracy and high interpretability offers a significant advance over opaque, single-classification verifiers. We will publically release code, data, prompts, and other artifacts upon acceptance.

## introduction large language models (llms) frequently hallucinate, producing content that is factually unsupported or incorrect @xcite @xcite . even when grounded in source documents, llm-generated summaries may contain contradictions or unverifiable statements, which can mislead readers and contribute to the spread of misinformation @xcite . ensuring the factual consistency of such outputs is therefore critical, particularly in * equal contribution domains where accuracy is paramount. in addition to raw accuracy, the explainability of these classifiers is increasingly important for transparency and human validation @xcite . without clear rationales, even correct predictions may be difficult to trust, and incorrect ones may be difficult to diagnose. existing work on grounded factuality verification spans a variety of strategies, including entailment-based classification, questionanswering formulations, and more recent llmdriven verification pipelines @xcite . while these approaches have achieved strong results in certain settings, they often provide limited transparency into the inferences behind complex judgments, and struggle with claims that require multi-hop reasoning across dispersed evidence @xcite . additionally, despite the lack of fine-grained classification and weak interpretability, frontier llms with few-shot prompting can achieve top-tier performance even compared to the strongest specialized baselines, but still have room for improvement in complex reasoning tasks @xcite . this highlights the need for strong-performing and explainable verification methods that are robust to challenging multi-hop reasoning. we propose a dual-path verification framework that decomposes a generated summary into subclaims and organizes verification results in an interpretable claim tree, with the summary as the root. the summary is first decomposed and decontextualized with an llm to preserve original, ensuring potential hallucinations are not either introduced or inadvertently corrected away. the system then filters out unverifiable subclaims (advice, opinions, or other statements not containing factually verifiable assertions) before classifying each subclaim as extractive (directly checkable against the source) or inferential (requiring multi-hop reasoning over evidence to verify). extractive subclaims are verified using two existing lightweight nli-based hallucination detectors, lettucedetect (ádám @xcite and minicheck-ft5 @xcite , followed by evidence retrieval for ease of understanding. inferential subclaims trigger a reasoning pathway that gathers supporting facts from the source and beyond, which may include textual evidence, mathematical reasoning, logical inference, or unrelated elementary knowledge that need not be verified. we attach the chain-of-thought trace from the llm when verifying the subclaim based on the supporting facts in the tree to boost auditability and ease of understanding. more importantly, these supporting facts are developed by the llm into a natural program -a natural language-based chain of reasoning which explicitly sets out premises and rigorously demonstrates how they are composed to support the claim @xcite . the reasoning of this natural program is then executed and validated step-by-step by an llm for verification, with dynamic error correction applied to detected mistakes in the program. the verification process is represented as a hierarchical claim-tree for easy visualization, where the root corresponds to the full summary, subclaims are the children of the root, and each subclaim's children are either the evidence or supporting facts which motivate its classification, making the motivation for each subclaim's classification clear. the summary is deemed supported only if all verifiable subclaims (extractive and inferential) are supported, ensuring conservative, evidence-grounded judgments that expose the full chain of evidence and reasoning behind complex decisions, yielding stronger reliability and greater interpretability than prior single-pass verifiers. our primary contributions are: entailment-based methods. a major line of work frames grounded factuality classification as natural language inference (nli) @xcite . several methods finetune an nli model to predict the faithfulness of a generated claim, sentence, or summary against the ground-truth source documents @xcite @xcite @xcite . these approaches are very efficient compared to methods involving llms but often offer poor interpretability due to the low granularity of the classification and the lack of a provided rationale. this can often leave what is truly causing the classification ambiguous to humans. to combat this, some methods operate at a token or span level (ádám @xcite @xcite . these provide a finer granularity of classification, often helping pinpoint the precise subclaims which are not faithful. however, since these methods only operate on spans of the generated text, when multi-hop reasoning is involved, they may still fail to reveal the intermediate inferences or supporting facts that connect the evidence to the claim, as such implicit inferences do not appear in the generated text. qa-based metrics. another prominent class of faithfulness evaluation methods frames factuality checking as a question-answering (qa) problem @xcite @xcite . in these approaches, a set of questions is typically generated from the summary. a qa model is then used to answer these questions given the source documents, and the answers are compared to the corresponding content in the summary @xcite . high overlap or semantic similarity indicates factual consistency, whereas discrepancies signal potential hallucinations. while qa-based metrics offer the advantage of explicitly tying verification to discrete factual questions, multi-hop reasoning may still be unexplored or left implicit as the reasoning required to support or refute a claim is often only able to emerge indirectly through evidence from the source rather than being explicitly modeled from the surfacelevel claims. additionally, effectively verifying how inferences over evidence connect to multi-hop claims may require reasoning over both the source and the summary, rather than relying on isolated question-answer pairs. ## methodology our claim verification framework operates through a multi-stage process that decomposes generated text into subclaims, verifies each subclaim according to its relation to the evidence, and organizes the verification results in an interpretable tree structure. given a summary, the framework constructs a tree where the root node represents the full summary, intermediate nodes represent subclaims, and leaf nodes contain evidence snippets or supporting facts. ## conclusion we present a hierarchical claim verification framework that advances the state-of-the-art in hallucination detection by providing both accurate classifica-tion and human-interpretable explanations. unlike black-box approaches that output only binary classifications, our framework makes the verification process transparent through a tree-based structure that traces the pipeline from claim decomposition to evidence gathering and reasoning. our method provides several key advantages. it offers fine-grained explainability by attaching concrete evidence or inferences to each subclaim and by generating natural programs-explicit, natural language reasoning chains that demonstrate how inferential claims are logically supported. this hybrid verification design combines lightweight transformer-based models for extractive verification with llm-based reasoning for more complex inferential claims, organized in a natural tree structure for clarity. experimental evaluation across four diverse datasets-aggrefact-cnn, aggrefact-xsum, to-fueval, and wice-demonstrates the effectiveness of our approach. our method achieves competitive performance with an average balanced accuracy of 74.6%, exceeding gpt-4o while providing detailed reasoning traces that enhance transparency. this combination of strong performance with explicit reasoning via natural programs represents a significant advance over existing black-box approaches. such transparency is crucial for practical deployment, where understanding not just whether a claim is supported but also why it is supported or refuted can help identify weaknesses, build trust, and improve reliability in real-world applications.