Reassess Summary Factual Inconsistency Detection with Large Language Model (2024)

Abstract: Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection. However, early attempts have shown that LLMs underperform traditional models due to their limited ability to follow instructions and the absence of an effective detection methodology. In this study, we reassess summary inconsistency detection with LLMs, comparing the performances of GPT-3.5 and GPT-4. To advance research in LLM-based inconsistency detection, we propose SIFiD (Summary Inconsistency Detection with Filtered Document) that identify key sentences within documents by either employing natural language inference or measuring semantic similarity between summaries and documents.

## introduction document summarization, the process of distilling key information from extensive texts, has become indispensable across various real-world applications, propelled by advancements in natural language generation (nlg) @xcite . the advent of large language models (llms) @xcite @xcite has notably enhanced models' capabilities to generate natural and factually consistent summaries @xcite . however, the rapid evolution in summarization techniques may lead to factually inconsistent summaries which are very close to facts @xcite . such inconsistencies could pose significant challenges, resulting in hallucinations that traditional detection models struggle to identify. as llms evolve, there is a critical demand for more robust methods to detect factual inconsistencies, leveraging the advanced capabilities of llms themselves. @xcite were among the first to utilize llms for the detection of factual inconsistencies, employing a universal zero-shot prompt across various benchmarks in summac @xcite and inputting the full document along with its summary into gpt-3.5 for evaluation. despite these innovations, their approach was limited by the plain application, early gpt-3.5 model's constraints and a lack of adaptation to the specific requirements of different benchmarks. consequently, their method did not achieve superior performance compared to existing models, such as those detailed in the summac paper. this paper revisits the challenge of inconsistency detection in document summarization through zeroshot inference with llms, specifically examining the latest versions of gpt-3.5 and gpt-4 on the summac dataset. we aim to set up new llmbased baselines for research in this domain. moreover, we introduce a novel methodology, sifid (summary inconsistency detection with filtered document), designed to significantly enhance the efficiency and effectiveness of factual inconsistency detection. sifid focuses on identifying crucial sentences within documents by evaluating their entailment scores or semantic similarity with summary sentences, subsequently retaining only the most relevant sentences for further analysis. this approach not only refines the assessment of factual consistency but also reduces the computational resources required for evaluation by decreasing the number of input tokens. our comprehensive evaluation on the summac dataset reveals that, while the updated gpt-3.5 model still falls short of outperforming traditional ## related work the evaluation of summary factual consistency has traditionally relied on methods such as question answering and question generation (qag) @xcite @xcite , synthetic classifiers @xcite , and pairing-based approaches @xcite . these methodologies focus on identifying discrepancies between documents and their summaries. @xcite later demonstrated that natural language inference (nli) could be effectively employed for inconsistency detection at appropriate levels of text granularity, thereby advancing the field of summary inconsistency detection. the emergence of large language models (llms) has recently shifted the focus towards integrating these models into the assessment of summary factual consistency. @xcite pioneered the application of gpt-3.5 for this purpose, tailoring prompts to various evaluation tasks including summary factual inconsistency detection, summary ranking, and consistency evaluation. despite this innovative approach, the early iteration of gpt-3.5, coupled with an insufficient detection methodology, did not yield improvements over conventional techniques in identifying factual inconsistencies. in our research, we revisit the approach proposed by @xcite , employing the most recent versions of gpt-3.5 and gpt-4. we integrate these advanced llms with our newly developed sum-mary inconsistency detection with filtered document (sifid) method. this combination aims to enhance the accuracy and efficiency of factual inconsistency detection, leveraging the state-of-theart capabilities of llms to set new benchmarks in the field. ## approach in this section, we detail our approach to reevaluating summary factual consistency using the latest gpt models and introduce the novel sifid method. ## experiments in this section, we detail the experiments conducted with gpt models and the sifid method on summac @xcite . we evaluated the performance of gpt-3.5, gpt-4, and sifid against a range of state-of-the-art approaches, including traditional methods such as dae @xcite , feqa @xcite , questeval @xcite , summac-zs, summac-conv @xcite , and an llmbased method proposed by @xcite . following previous research @xcite , we report the balanced accuracy for summac. the experimental results were obtained from @xcite . our experiments utilized gpt-3.5-turbo-1106 and gpt-4-1106-preview 2 . for the sifid configuration, we applied β = 0.0 for entailment-based filtering and β = 0.5 for semantic similarity-based filtering, observing a 61.3% and 67% sentence removal rate on average across benchmarks, respectively. we use all-mpnet-base-v2 for sentencetransformers. ## conclusion in this study, we advance the field of llm-based summary factual inconsistency detection by evaluating the performance of the latest gpt models, thereby establishing new benchmarks for future research. we introduce sifid, a novel, efficient, and effective approach that computes a relevance matrix at the sentence level between the document and its summary. this method filters out irrelevant sentences from the document before employing llms for inconsistency detection. our experimental findings on the summac dataset demonstrate that sifid significantly enhances the performance of advanced gpt models in detecting factual inconsistencies, highlighting its potential to facilitate more accurate and resource-efficient research in this domain.