Summary of Finetunebench: How Well Do Commercial Fine-tuning Apis Infuse Knowledge Into Llms?, by Eric Wu et al.
FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?
by Eric Wu, Kevin Wu, James Zou
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 The paper introduces FineTuneBench, an evaluation framework and dataset for assessing the effectiveness of commercial fine-tuning APIs in updating existing knowledge and infusing new information into large language models (LLMs). The study analyzes five frontier LLMs with commercially available fine-tuning APIs on their ability to learn new information and update existing knowledge. The results reveal substantial shortcomings in all the models’ abilities to effectively learn new information, with an average generalization accuracy of 37%. When updating existing knowledge, the average generalization accuracy drops to 19%. The study highlights a major shortcoming in using current commercial fine-tuning services to achieve reliable knowledge infusion in common scenarios. The FineTuneBench dataset is open-sourced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well big language models can learn new things and keep old knowledge up to date. Researchers compared five of these models with special tools that help them learn from new information. They found that all the models struggled to learn new things, getting only about 37% right on average. When it came to updating old knowledge, they did even worse, getting only around 19% correct. The study shows that we need better ways to fine-tune these models so they can really learn and remember new things. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
