Successful Language Model Evaluations and Their Impact
Published:May 24, 2024 19:45
•1 min read
•Jason Wei
Analysis
This article highlights the importance of evaluation benchmarks (evals) in driving progress in the field of language models. The author argues that evals act as incentives for the research community, leading to breakthroughs when models achieve significant performance improvements on them. The piece identifies several successful evals, such as GLUE/SuperGLUE, MMLU, GSM8K, MATH, and HumanEval, and discusses how they have been instrumental in advancing the capabilities of language models. The author also touches upon their own contributions to the field with MGSM and BBH. The key takeaway is that a successful eval is one that is widely adopted and trusted within the community, often propelled by a major paper showcasing a significant achievement using that eval.
Key Takeaways
- •Evaluation benchmarks are crucial for driving progress in language models.
- •Successful evals are widely adopted and trusted within the research community.
- •Major papers showcasing significant achievements on evals contribute to their success.
Reference
“Evals are incentives for the research community, and breakthroughs are often closely linked to a huge performance jump on some eval.”