Summary of Evaluating the Efficacy Of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-tuning Decision-making, by Oluyemi Enoch Amujo and Shanchieh Jay Yang
Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making
by Oluyemi Enoch Amujo, Shanchieh Jay Yang
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)
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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 evaluates large language models (LLMs) Gemma-2B and Gemma-7B on diverse domains, including cybersecurity, medicine, and finance. The authors compare model performance on commonplace queries to domain-specific prompts, which is crucial for benchmarking prior to fine-tuning for specific tasks. The study utilizes a comprehensive methodology, including ThroughCut, a novel outlier detection technique that identifies response throughput outliers based on conciseness. The evaluation assesses inference time, response length, throughput, quality, and resource utilization, revealing significant correlations between these factors. Model size and prompt types significantly impact response length and quality, while domain-specific prompts generate consistent responses within reasonable times. Overall, this study highlights the need for comprehensive evaluation frameworks to ensure reliable benchmarking procedures in multidomain AI research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models do when asked questions about different topics like cybersecurity, medicine, and finance. They want to see if these models are better at answering questions that are specific to certain areas or if they can handle everyday queries just as well. To do this, the authors use a special method called ThroughCut that helps identify unusual responses. The study looks at how long it takes for the model to give an answer, how long the answer is, and other factors like how much computer power it uses. They find that the size of the model and the type of question asked affect how good the answer is and how fast the model can respond. Overall, this study shows why we need better ways to test these language models so they can be used in real-life situations. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Outlier detection * Prompt
