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 in large amounts of textual data and display extraordinary abilities in Natural Language Processing (NLP) and Natural Language Understanding (NLU). Using deep learning, LLMs process natural language and excel at language-related tasks.
LLMs like ChatGPT and PaLM do extremely well on invisible tasks with the help of proper instructions or task definition. They even use Chain-of-Thought (CoT) prompts to improve their performance on such tasks, which is a prompting method that allows an LLM to explain their reasoning. CoT prompts provide the model with a series of related prompts to guide its responses.
In a recently published research paper, the authors discussed the performance of ChatGPT and how to assess its overall ability to perform detailed 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, such as 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 responses requires evaluating your ability to achieve high performance and measuring the reliability of your responses. To help users better understand the overall quality of ChatGPT responses, the article’s authors have designed four metric dimensions: performance, explainability, calibration, and fidelity. Performance refers to the overall performance of ChatGPT in various IE tasks from numerous perspectives. Explainability evaluates whether or not ChatGPT can provide a justified reason for its prediction. It provides information about your decision-making process. Calibration measures the predictive uncertainty of a model and assesses whether ChatGPT is overconfident in its prediction. Finally, Fidelity determines if the explanations provided by ChatGPT are true to the input or if they are false.
The authors have carried out their experiments and analyzes based on 14 data sets belonging to 7 detailed IE tasks, some of which include named entity recognition (NER), relationship extraction (RE) and extraction. of events (EE). The results show that ChatGPT’s performance in standard IE settings is poor, causing it to have problems with tasks that require the extraction of structured information. On the other hand, it shows excellent performance in OpenIE configuration, which involves extracting information from unstructured text. These results were evidenced by human evaluation, where human evaluators rated the ChatGPT responses as appropriate and of high quality.
The authors have shared how ChatGPT provides high-quality and reliable explanations for its decisions, but its over-confident nature results in low calibration, that is, its predicted probabilities do not match the actual probabilities. ChatGPT shows a high level of fidelity to the original text in most cases and is therefore true to the meaning and intent of the original text.
In conclusion, this research provides a valuable framework for evaluating ChatGPT and similar LLMs, allowing users to better understand the overall quality of their responses. A study of the information extraction capabilities of ChatGPT: evaluation of its performance, explainability, calibration and fidelity
review the Paper. Don’t forget to join our 20k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
🚀 Check out 100 AI tools at AI Tools Club
Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
