The article evaluates the performance and behavior of two large language models (LLMs), GPT-3.5 and GPT-4, on various tasks such as math problems, sensitive questions, opinion surveys, code generation, medical exams, and visual reasoning. The researchers find that both models' performance and behavior can vary significantly over time. For instance, GPT-4's accuracy in identifying prime vs. composite numbers dropped from 84% in March 2023 to 51% in June 2023. On the other hand, GPT-3.5 improved its performance in some tasks but became less willing to answer sensitive questions and opinion surveys. Additionally, both models made more formatting mistakes in code generation in June compared to March.
The study emphasizes the need for continuous monitoring of LLMs as their behavior can change rapidly. The authors also highlight the challenge of integrating LLMs into larger workflows due to the potential disruption of downstream pipelines when LLM responses change. The findings suggest that updates aimed at improving certain aspects of LLMs can lead to a decrease in performance in other dimensions. Therefore, the authors recommend implementing similar monitoring analyses for applications relying on LLM services to ensure consistent performance over time.
The scientific article discussed in this passage is titled "Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task" and was written by Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, and others. It was published as a preprint on arXiv in 2018.
The article introduces the Spider dataset, a large-scale dataset labeled by humans. Its purpose is to support complex and cross-domain semantic parsing and text-to-SQL tasks. The dataset encompasses a wide range of examples across various domains, including academic, business, and geography.
The authors emphasize the importance of having a large-scale labeled dataset for training and evaluating models in the field of natural language processing. They explain that the Spider dataset can be used to train models to understand natural language queries and generate SQL queries for extracting information from databases.
The article presents the results of experiments conducted using the Spider dataset. The authors compare the performance of different models on the dataset and evaluate their ability to accurately parse and understand complex queries. They find that their proposed model, which incorporates both reasoning and acting capabilities, outperforms other models on the Spider dataset.
Additionally, the article briefly mentions two other scientific articles: "React: Synergizing reasoning and acting in language models" by Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao, and "How language model hallucinations can snowball" by Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A Smith. However, it does not provide further details about these articles.
The passage also includes examples of GPT-4's generation in March for determining whether certain numbers are prime. These examples demonstrate how GPT-4 calculates the square root of the numbers and checks for divisibility by prime numbers. It notes that while GPT-4's reasoning steps were mostly correct, there was one arithmetic mistake in one of the examples.
Lastly, the article briefly mentions the counting of happy numbers within smaller intervals and presents a confusion matrix shift for this task. The results show that GPT-4's March version generated mostly correct answers for the queries.
In summary, the discussed scientific article introduces the Spider dataset for complex and cross-domain semantic parsing and text-to-SQL tasks. It presents the results of experiments using the dataset and compares the performance of different models. The passage also includes examples of GPT-4's generation for prime numbers and discusses the counting of happy numbers within smaller intervals.
Reference: https://arxiv.org/abs/2307.090...