Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness (2304.11633v1)

Abstract: The capability of LLMs like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately. In this paper, we focus on assessing the overall ability of ChatGPT using 7 fine-grained information extraction (IE) tasks. Specially, we present the systematically analysis by measuring ChatGPT's performance, explainability, calibration, and faithfulness, and resulting in 15 keys from either the ChatGPT or domain experts. Our findings reveal that ChatGPT's performance in Standard-IE setting is poor, but it surprisingly exhibits excellent performance in the OpenIE setting, as evidenced by human evaluation. In addition, our research indicates that ChatGPT provides high-quality and trustworthy explanations for its decisions. However, there is an issue of ChatGPT being overconfident in its predictions, which resulting in low calibration. Furthermore, ChatGPT demonstrates a high level of faithfulness to the original text in the majority of cases. We manually annotate and release the test sets of 7 fine-grained IE tasks contains 14 datasets to further promote the research. The datasets and code are available at https://github.com/pkuserc/ChatGPT_for_IE.

Evaluating ChatGPT’s Information Extraction Capabilities: An Expert Review

The research paper "Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness" presents a comprehensive evaluation of ChatGPT, a prominent LLM, in the domain of Information Extraction (IE). This analysis is structured across four primary dimensions: performance, explainability, calibration, and faithfulness, using seven fine-grained IE tasks.

Performance Evaluation

Performance is a critical evaluation criterion, particularly in the context of standardized data-driven NLP tasks, such as those found in IE. The authors evaluate ChatGPT's capabilities on 14 datasets spanning seven different IE tasks. These include entity typing, named entity recognition, relation classification, relation extraction, event detection, event argument extraction, and event extraction. The results reveal that ChatGPT underperforms in the Standard-IE settings compared to supervised BERT-based models. However, ChatGPT shows surprising proficiency in the OpenIE settings, implying its capability to apply generalized knowledge when not constrained by pre-defined label sets. In the OpenIE scenario, human judges found ChatGPT’s outputs reasonable across multiple datasets, particularly in less complex tasks like ET and NER. This suggests ChatGPT’s potential utility as a candidate generator in unsupervised settings.

Explainability

The explainability of a system, particularly in complex decision-making environments like those powered by LLMs, is paramount to ensure trustworthiness. In their assessment, the authors examine ChatGPT’s ability to provide human-interpretable reasons for its predictions under both self-check and human-check protocols. The findings indicate a high level of congruence between ChatGPT-generated explanations and expert human judgments, suggesting that ChatGPT can articulate convincing justifications for its decisions in both Standard-IE and OpenIE settings. This can significantly facilitate users' understanding of model outputs and improve the adoption of LLMs in real-world applications.

Calibration

Within the context of probabilistic predictions, calibration reflects the reliability of the model’s confidence scores. Disconcertingly, ChatGPT displays significant overconfidence, resulting in poor calibration compared to BERT-based models. This pronounced overconfidence is evidenced by the notable disparity between its correct and incorrect predictions' confidence levels. The authors measure this using the Expected Calibration Error (ECE), where ChatGPT’s deviation from well-calibrated predictions highlights a critical area for improvement. Improved calibration could enhance the model's utility, particularly in high-stakes environments requiring accurate uncertainty quantifications.

Faithfulness

Faithfulness examines the alignment between explanations provided and the original text. This dimension underscores the model’s integrity, ensuring that generated explanations do not mislead users. The high faithfulness scores across evaluated tasks indicate ChatGPT’s explanations are generally truthful, which is crucial for maintaining user trust in outputs. Given the potential for LLMs to generate "hallucinations," maintaining high faithfulness is essential in ensuring users receive accurate and contextually relevant information.

Implications and Future Developments

This paper’s findings carry significant implications for the deployment of LLMs like ChatGPT in IE tasks. While the model excels in tasks that do not rely heavily on precise calibration and can provide satisfactory explanatory content, its current limitations in miscalibration prompt a need for enhancements in this area. Further research might focus on iterative improvements to the model's calibration techniques to align its confidence scores more closely with predictive correctness. Additionally, improving ChatGPT's performance in more complex IE tasks could lead to its broader application across diverse domains requiring nuanced language understanding and extraction capabilities.

In conclusion, while ChatGPT shows promising results, especially in open information environments, the research outlines crucial areas for improvement, notably in calibration and performance in complex tasks. Addressing these will be key to maximizing the practical utility of LLMs in diverse and challenging real-world scenarios.