This AI Paper Proposes to Systematically Analyse the ChatGPT’s Performance, Explainability, Calibration, and Faithfulness

ChatGPT, developed by OpenAI, is currently the most popular Large Language Model (LLM) that understands human intent. It generates good-quality content and is famous for having human-like conversations. LLMs are trained on a huge amount of textual data and show extraordinary capabilities in Natural Language Processing (NLP) and Natural Language Understanding (NLU). Using deep learning, LLMs process natural language and excel in language-related tasks.  

LLMs like ChatGPT and PaLM perform extremely well on unseen tasks with the help of proper instruction or task definition. They even use Chain-of-Thought (CoT) prompting to improve their performance on such tasks, which is a prompting method that enables an LLM to explain its reasoning. CoT prompting provides the model with a series of related prompts to guide its responses.

In a recently released research paper, authors have discussed ChatGPT’s performance and the way to assess its overall ability to perform fine-grained information extraction (IE) tasks. Information extraction (IE) is the process of automatically extracting specific information, such as structured information, from an unstructured or semi-structured data source like a body of text. It extracts heterogeneous structures, using factual knowledge, and targeting diverse information, making it an ideal scenario for evaluating ChatGPT’s capabilities.

Evaluating ChatGPT’s responses requires assessing its ability to achieve high performance and measuring its answers’ reliability. To help users better understand the overall quality of ChatGPT’s responses, the authors of the paper have designed four metric dimensions: Performance, Explainability, Calibration, and Faithfulness. Performance refers to the overall performance of ChatGPT on various IE tasks from numerous perspectives. Explainability evaluates whether ChatGPT can provide a justified reason for its prediction or not. It provides insights into its decision-making process. Calibration measures the predictive uncertainty of a model and assesses if ChatGPT is overconfident in its prediction. Lastly, Faithfulness determines whether the explanations provided by ChatGPT are truthful to the input or if they are false. 

The authors have conducted their experiments and analysis based on 14 datasets belonging to 7 fine-grained IE tasks, some of which include named entity recognition (NER), relation extraction (RE), and event extraction (EE). The results show that ChatGPT’s performance in the Standard-IE setting is poor, so it struggles with tasks requiring structured information extraction. On the other hand, it exhibits excellent performance in the OpenIE setting, which involves extracting information from unstructured text. These results were evidenced by human evaluation, where human evaluators rated ChatGPT’s responses as being high-quality and appropriate.

The authors have shared how ChatGPT provides high-quality and trustworthy explanations for its decisions, but its overconfident nature results in low calibration, i.e., its predicted probabilities do not match actual probabilities. ChatGPT portrays a high level of Faithfulness to the original text in most cases and is thus faithful to the meaning and intent of the original text. 

In conclusion, this research provides a valuable framework for evaluating ChatGPT and similar LLMs, enabling users to better understand their responses’ overall quality. A Study of ChatGPT’s Information Extraction Abilities: Assessing its Performance, Explainability, Calibration, and Faithfulness

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