Summary of Enterprise Benchmarks For Large Language Model Evaluation, by Bing Zhang et al.
Enterprise Benchmarks for Large Language Model Evaluation
by Bing Zhang, Mikio Takeuchi, Ryo Kawahara, Shubhi Asthana, Md. Maruf Hossain, Guang-Jie Ren, Kate Soule, Yada Zhu
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the pressing need for rigorous evaluation of large language models (LLMs) in enterprise applications, particularly when it comes to benchmarking complex tasks using domain-specific datasets. The authors propose a systematic framework for evaluating LLMs on a diverse set of 25 publicly available datasets from various enterprise domains, including financial services, legal, cyber security, and climate and sustainability. This framework encompasses a range of NLP tasks and showcases the varying performance of 13 models across different enterprise applications. By highlighting the importance of selecting the right model for specific requirements, this work emphasizes the need for benchmarking strategies tailored to LLM evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can make sure that big language models are doing a good job in real-world settings like businesses and companies. Right now, it’s hard to tell if these models are working well or not, so the authors came up with a plan to test them using lots of different data sets from various industries. They looked at 25 sets of data, covering things like finance, law, cybersecurity, and climate change. By testing 13 different language models on these tasks, they showed that each model is good at certain things but not others. This helps us choose the right model for a specific job. |
Keywords
» Artificial intelligence » Nlp
