Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics (2024)

Abstract: Large language models (LLMs) often produce unsupported or unverifiable content, known as “hallucinations.” To mitigate this, retrieval-augmented LLMs incorporate citations, grounding the content in verifiable sources. Despite such developments, manually assessing how well a citation supports the associated statement remains a major challenge. Previous studies use faithfulness metrics to estimate citation support automatically but are limited to binary classification, overlooking fine-grained citation support in practical scenarios. To investigate the effectiveness of faithfulness metrics in fine-grained scenarios, we propose a comparative evaluation framework that assesses the metric effectiveness in distinguishing citations between three-category support levels: full, partial, and no support. Our framework employs correlation analysis, classification evaluation, and retrieval evaluation to measure the alignment between metric scores and human judgments comprehensively. Our results show no single metric consistently excels across all evaluations, revealing the complexity of assessing fine-grained support. Based on the findings, we provide practical recommendations for developing more effective metrics.

## introduction large language models (llms) often generate content known as "hallucinations" @xcite @xcite , which contradicts established knowledge or lacks verification from reliable sources. mainstream studies @xcite aim to mitigate this by using retrieval-augmented llms to generate responses with in-line citations that provide supporting evidence. one primary challenge is to assess how well a citation supports its statement, as manual evaluation is labor-intensive and time-consuming. automated citation evaluation has been explored to reduce reliance on human assessments @xcite . to this end, faithfulness evaluation metrics are employed as proxies to automatically estimate the citation support @xcite . these metrics measure the faithfulness between model-generated and sourced text, which aligns closely with the objectives of automated citation evaluation. prior studies in faithfulness metrics have primarily limited this task to a binary classification problem @xcite , where faithfulness metrics are leveraged to determine whether a citation supports the associated statement. however, this binary approach fails to capture the fine-grained citation support encountered in real-world applications. for instance, in figure 1 , a retrieval-augmented llm generates a response with multiple citations given a query. a human assessor labels the first citation as "partial support" since it only supports "the most humid place in australia is macquarie island" but not "which is located in the southern ocean off the coast of tasmania." this partial support scenario causes noticeable inconsistencies across three dif-ferent faithfulness metrics. therefore, there is a significant research need to evaluate the effectiveness of faithfulness metrics in accurately distinguishing citations in such fine-grained support scenarios. to address this issue, we propose a comparative evaluation framework for assessing the metric effectiveness in fine-grained support scenarios. in our framework, we define "support levels" as the extent to which a citation supports the associated statement @xcite . specifically, we consider a three-category support level scenario: full, partial, and no support. these categories indicate whether a citation fully, partially or does not support the associated statement, respectively. to comprehensively assess the metric effectiveness, we measure the alignment between metric scores and human judgments with three types of evaluation protocols: 1) correlation analysis: we employ it to measure how well metric scores correlate with human judgments. 2) classification evaluation: we conduct a classification evaluation to assess the metrics' capability to distinguish citations based on their support levels. 3) retrieval evaluation: we undertake a retrieval evaluation to assess the metric effectiveness in ranking citations according to their support levels. this is motivated by the observation that the previous two evaluation protocols assume citations are within statements, which is not always valid in practice @xcite . in such cases, faithfulness metrics are adapted to perform post-hoc retrieval, aiming to retrieve potential citations from a candidate pool @xcite . thus, retrieval evaluation is crucial for determining the practical utility of these metric adaptations. in our experiments, we assess various widely used faithfulness metrics, categorizing them into similarity-based, entailment-based, and llmbased metrics. we find that: 1) no single faithfulness metric consistently outperforms others across three evaluation protocols, suggesting that these protocols are complementary and should be integrated for a comprehensive evaluation of metric performance; 2) the best-performing metrics show promise in distinguishing some support scenarios but struggle with others. this highlights the inherent complexities of automated citation evaluation. 3) similarity-based metrics surpass best-performing entailment-based metrics in retrieval evaluation. this indicates that entailment-based metrics exhibit higher sensitivity to noisy data, which is introduced by irrelevant documents in such scenarios. our contributions can be summarized as follows: ## related work faithfulness evaluation metrics faithfulness evaluation metrics are crucial for assessing the factual consistency of text generated by models relative to the source text. it receives great interest within the field of natural language generation (nlg) @xcite @xcite @xcite , particularly in abstractive summarization @xcite @xcite @xcite . in general, faithfulness metrics are categorized into three types: entailment-based, similarity-based, and qa-based metrics. entailment-based metrics employ natural language inference (nli) models to determine if the source text entails the generated text @xcite @xcite . similarity-based metrics, such as bertscore @xcite and bartscore @xcite , quantify text similarity and have demonstrated robust performance in faithfulness evaluation @xcite . qa-based metrics utilize a combination of question generation and question answering to estimate faithfulness levels @xcite @xcite . in this work, we exclude qa-based metrics from our work, following recent works suggesting the challenging limitations in these metrics @xcite . we focus on the extrinsic evaluation of faithfulness metrics against human judgments in scenarios requiring fine-grained citation support. ## evaluation framework in this section, we introduce the proposed comparative evaluation framework. we begin by formalizing the task of automated citation evaluation. subsequently, we detail three distinct evaluation protocols within this framework, ensuring a comprehensive assessment in alignment between faithfulness metrics and human judgments. our framework is demonstrated in figure 2 . ## faithfulness metrics in our experiments, we evaluate diverse faithfulness evaluation metrics, dividing them into similarity-based, entailment-based, and llmbased metrics. similarity-based metrics assess the support levels mainly based on the degree of similarity between the citation and the associated statement. entailment-based metrics leverage pretrained nli models to estimate the support levels. llm-based metrics directly prompt llms to measure the support levels. ## experiments in this section, we describe the dataset used in the experiments. subsequently, we discuss the evaluation metrics incorporated within our proposed framework, which assess the performance of faithfulness metrics in alignment with human judgments. ## results and analyses in this section, we discuss the performance of faithfulness metrics across three distinct evaluation protocols. subsequently, we conduct a qualitative analysis through case studies. ## discussions overall, our results across three evaluation protocols indicate that the evaluation protocols are complementary and should be integrated for a comprehensive assessment of metrics. based on the evaluation results, we further propose the following practical recommendations to develop more effective metrics for automated citation evaluation: 1) development of training resources: motivated by the observation that the best-performing metrics still struggle with identifying partial support, we recommend the development of training resources that include fine-grained support level annotations. these resources could significantly enhance the metrics' fine-grained sensitivity to varying support levels; 2) introduction of contrastive learning: to improve the robustness of metrics in post-hoc retrieval scenarios, we recommend fine-tuning metrics using contrastive learning frameworks. this method has demonstrated effectiveness across various information retrieval tasks @xcite . ## conclusion we propose a comparative evaluation framework to explore the efficacy of faithfulness metrics beyond the binary scenario by examining three levels of citation support. our framework employs correlation analysis, classification evaluation, and retrieval evaluation to measure the alignment between metric scores and human judgments. experimental results reveal that no single metric consistently excels across all evaluation protocols, indicating the complexity of automated citation evaluation and the limitations of existing faithfulness metrics. we provide practical suggestions based on the findings.