ACUE val: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization (2024)

Abstract: The impressive generation capabilities of large language models (LLMs) have made it harder to detect the subtle hallucinations they make in abstractive summarization, where generated summaries consist of a blend of correct and incorrect information w.r.t. a given document. Recently-proposed LLM-based evaluation metrics attempt to capture this, but still face challenges: (1) they are biased towards summaries generated from the same underlying LLM, and (2) they lack interpretability, offering only a single score. In this work, we present ACUEval, a metric that leverages the power of LLMs to perform two sub-tasks: decomposing summaries into atomic content units (ACUs), and validating them against the source document. Compared to current strong LLM-based metrics, our two-step evaluation strategy improves correlation with human judgments of faithfulness on three summarization evaluation benchmarks by 3% in balanced accuracy compared to the next-best metric, and also shows reduced preference bias towards LLM-generated summary. Further, we show that errors detected by ACUEval can be used to generate actionable feedback for refining the summary, improving the faithfulness scores by more than 10%.

## introduction hallucination in abstractive summarization, where the generation contains information that is inconsistent with the source document, remains a crucial problem despite the significant progress of large language models (llm) @xcite . the problem has become more subtle, as the generations often contain a mixture of correct and hallucinated facts @xcite , making the detection of such errors harder. recently-proposed evaluation metrics have achieved high correlations with human preferences with the aid of llms @xcite . nevertheless, similar to the observation by @xcite and @xcite , we find that such metrics generally have a preference-bias, where the metric favors generations from the same underlying llm used for scoring. furthermore, such metrics often output only a single numeric score, making them less interpretable to practitioners in understanding the precise location of the errors and the justification behind the score. to address these problems, we present a new metric: acueval, which leverages the strong capability of llms to perform two fine-grained and structured sub-tasks instead of asking the model to directly provide a single score. we operate on the level of atomic content units @xcite , facts that can be verified and cannot be broken down further. acueval first generates these atomic facts from the system summary, and then validates each extracted fact against the source document. in figure 1 , we show that acueval successfully identifies that the second atomic fact is not consistent with the source document. operating on such fine-grained units as an intermediate representation instead of directly on the system summary reduces the preference bias of the metric in assigning high scores for summaries generated by the same underlying model. acue-val involves two separate steps, each drawing on different input sources. the first step, acu generation, relies solely on the system summary, while the second step, acu verification, evaluates the consistency of the acu with respect to the original document without the use of the summary. this separation ensures that the model does not implicitly assign the best score for the outputs generated by the same model. moreover, the systematic matching between all extracted facts and the source document narrows down the issue of hallu- stage 1: summary faithfulness evaluation candidate summary exploratory drilling for oil and gas on anglesey has been given the go-ahead by natural resources wales. atomic content units exploratory drilling for oil and gas has been approved. the location is on anglesey. the approval was given by natural resources wales. acueval exploratory drilling for oil and gas has been approved. the location is on anglesey. the approval was given by natural resources wales. document verify atomic units using document generate atomic units extract wrong facts from acu eval stage 2: summary refinement with acueval feedback wrong fact(s) the location is on anglesey. ## acueval figure 1 illustrates our metric acueval. here, we assume that we have a source document x and a generated summary ŷ. acueval consists of two structured steps: (1) deconstructing the summary into fine-grained acus, and (2) predicting the presence of each acu against the information presented in the source document. the result of these steps is a faithfulness score. acu generation. we first generate atomic content units (acus), or atomic facts, from the summary. we follow the definition of acus by @xcite : elementary information units, which no longer need to be further split for the purpose of reducing ambiguity in human evaluation. we note that, unlike previous approaches where atomic facts were generated from reference summary y, we apply this method to the generated summary ŷ. this approach yields more fine-grained information of the summary, which has been shown to improve faithfulness evaluation @xcite . additionally, we opt for a textual representation over complex representations like dependency parses @xcite or amr graphs @xcite , which simplifies the integration of error localization in llms. formally, we break down a summary ŷ into a list of atomic facts a ŷ = {a1, a2, ..., a n }. we generate these facts by asking an llm to break an utterance up using the prompt shown in figure 5 . ## benchmarking llm for faithfulness lastly, acueval can also serve as a powerful analytical tool for assessing the capacity of current large language models (llms) to generate faithful summaries. demonstrating strong correlations with human evaluations, particularly in recent models, acueval provides a practical and reliable alternative to human assessments of hallucinations. ## conclusion in this paper, we introduce acueval an interpretable, fine-grained metric for evaluating faithfulness for abstractive summarization. our findings demonstrate that acueval achieves the highest balanced accuracies across diverse benchmarks and datasets, outperforming other recent, strong llmbased metrics. notably, acueval shows very low bias towards llm-generated outputs, making it a fair tool for evaluation of summaries in the era of llms. next, we also explore how acus that are considered not faithful to the input document can be incorporated as detailed feedback, which in turn enhances the correction model at refining the summary with little hallucination. finally, we compare the average acueval scores of various llms, assessing their faithfulness in abstractive summarization. these comparisons align closely with human judgments and reveal that larger models tend to be more faithful. we hope that acueval can serve as a foundational guide for evaluating generated summaries. looking forward, we propose expanding this framework to encompass additional facets of summarization evaluation and adapting it for more complex tasks like multi-document and long-form summarization.