A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models (2303.10420v2)

Abstract: GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities. However, despite the abundance of research on the difference in capabilities between GPT series models and fine-tuned models, there has been limited attention given to the evolution of GPT series models' capabilities over time. To conduct a comprehensive analysis of the capabilities of GPT series models, we select six representative models, comprising two GPT-3 series models (i.e., davinci and text-davinci-001) and four GPT-3.5 series models (i.e., code-davinci-002, text-davinci-002, text-davinci-003, and gpt-3.5-turbo). We evaluate their performance on nine natural language understanding (NLU) tasks using 21 datasets. In particular, we compare the performance and robustness of different models for each task under zero-shot and few-shot scenarios. Our extensive experiments reveal that the overall ability of GPT series models on NLU tasks does not increase gradually as the models evolve, especially with the introduction of the RLHF training strategy. While this strategy enhances the models' ability to generate human-like responses, it also compromises their ability to solve some tasks. Furthermore, our findings indicate that there is still room for improvement in areas such as model robustness.

Capability Analysis of GPT-3 and GPT-3.5 Series Models: Performance and Robustness

This paper provides a comprehensive evaluation of the performance and robustness of six prominent models from the GPT-3 and GPT-3.5 series, focusing on their capabilities in natural language understanding (NLU) tasks. The authors analyze nine NLU tasks using data from 21 different datasets to discern how the models fare under zero-shot and few-shot scenarios. Importantly, the paper emphasizes the impact of various model training strategies on the models' ability to understand and generate language.

Summary of Methodology and Findings

The paper systematically evaluates models including davinci and text-davinci-001 from the GPT-3 line, and models like code-davinci-002, text-davinci-002, text-davinci-003, and gpt-3.5-turbo from the GPT-3.5 line. Using 21 datasets encompassing a broad range of NLU tasks—such as Aspect-based Sentiment Analysis (ABSA), Machine Reading Comprehension (MRC), Named Entity Recognition (NER), and more—the authors critically compare model performance on linguistic comprehension, task comprehension, and sensitivity to prompt variations.

A stark finding presented in the paper is that the evolution from GPT-3 to GPT-3.5 models does not correlate to a consistent performance increment across all tasks. Notably, methods such as Reinforcement Learning from Human Feedback (RLHF) enhance human-like response generation, yet sometimes at the expense of performance in certain structured tasks, such as MRC and NLU tasks. The "alignment tax," a term referenced by the paper, suggests potential trade-offs in performance induced by aligning models to reproduce human-like sentiment and responses.

Key Results

• Model Performance Variability: On tasks like NER and POS tagging, the results indicated substantial variance in model performance based on task characteristics. Code-davinci-002 exhibited strong performance in NER tasks, while gpt-3.5-turbo showed prominence in sentiment analysis tasks.
• Effect of Prompting Strategy: All models showed sensitivity to prompts, affecting their performance in zero-shot and few-shot scenarios. For example, prompt modifications significantly changed the outputs for tasks like Semantic Matching (SM) and Natural Language Inference (NLI).
• Robustness Analysis: Across evaluated tasks, robustness did not markedly improve with newer models. The paper points out that understanding and addressing model robustness remains an open challenge.

Implications for Future Research

This empirical evaluation provides depth to our understanding of the advantages and limitations of NLP systems, particularly in terms of generalization across tasks versus specializing in human-like dialogue and response. It underscores the need for continued refinement of LLMs to balance performance, robustness, and interpretability. As AI models become integral to applications requiring nuanced understanding and interaction, further research should explore enhancing these models' capabilities without compromising task-specific performance.

It also opens the floor for future research on other emerging models like GPT-4, mentioned as having yet to be evaluated thoroughly due to current access constraints.

In conclusion, while improvements in LLM designs are evident, particularly in interaction-focused applications, there is continued necessity for honing model training strategies to fortify robustness and deepen natural language understanding across variable contexts.