# GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond

**Authors:** Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Xun Zhou, Kevin Chen-Chuan Chang  
**arXiv:** [2309.16583](https://arxiv.org/abs/2309.16583)  
**Date:** 2023-09-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.16583v1/

## Key points

- Authors address the need for a comprehensive evaluation suite for large language models (LLMs)
- Existing LLM leaderboards lack consistent settings and prompts, leading to cherry-picking of results
- Introduce GPT-Fathom, an open-source and reproducible LLM evaluation suite built on OpenAI Evals
- Systematically evaluate over 10 leading LLMs on more than 20 curated benchmarks across seven capability categories
- All evaluations conducted under aligned settings to ensure fair comparisons
- Provide retrospective study on OpenAI's earlier models, offering insights into the evolutionary path from GPT-3 to GPT-4
- Community interested in understanding how GPT-3 improves to GPT-4 and which aspects can be improved by techniques like SFT and RLHF
- Explore impact of alignment tax
- Analysis and evaluations using GPT-Fathom contribute to improving transparency in advanced LLMs
- Work provides valuable insights into capabilities and limitations of different models
- Establish standardized framework for evaluating future advancements in large language models

## Summary

In their paper titled "GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond," authors Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Xun Zhou, and Kevin Chen-Chuan Chang address the need for a comprehensive evaluation suite to assess the capabilities and limitations of large language models (LLMs). They highlight that existing LLM leaderboards often lack consistent settings and prompts, leading to cherry-picking of favorable results. To address this issue, they introduce GPT-Fathom, an open-source and reproducible LLM evaluation suite built on OpenAI Evals. The authors systematically evaluate more than 10 leading LLMs, including OpenAI's legacy models, on over 20 curated benchmarks across seven capability categories. Importantly, all evaluations are conducted under aligned settings to ensure fair comparisons. Additionally, they provide a retrospective study on OpenAI's earlier models, offering valuable insights into the evolutionary path from GPT-3 to GPT-4. The community is particularly interested in understanding how GPT-3 progressively improves to GPT-4 as well as which aspects of LLM capability can be improved by techniques like SFT (Systematic Fine-Tuning) and RLHF (Reinforcement Learning from Human Feedback). Furthermore, they explore the impact of alignment tax. By addressing these questions through their analysis and evaluations using GPT-Fathom, the authors contribute to improving transparency in advanced LLMs. Their work provides valuable insights into the capabilities and limitations of different models while establishing a standardized framework for evaluating future advancements in large language models.