Summary of Evaluating Large Language Models For Material Selection, by Daniele Grandi et al.
Evaluating Large Language Models for Material Selection
by Daniele Grandi, Yash Patawari Jain, Allin Groom, Brandon Cramer, Christopher McComb
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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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 study investigates the use of Large Language Models (LLMs) for material selection in product design, comparing their performance with expert choices. A dataset of expert material preferences is collected to evaluate LLMs’ alignment with expert recommendations through prompt engineering and hyperparameter tuning. The results highlight two failure modes and identify parallel prompting as a useful method when using LLMs for material selection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses Large Language Models (LLMs) to help choose materials in product design. It compares how well the models work compared to what experts would choose. To do this, it collects data on expert preferences and tries different ways of asking questions (prompt engineering) and adjusting model settings (hyperparameter tuning). The results show that LLMs can be helpful but often give very different answers than humans. |
Keywords
» Artificial intelligence » Alignment » Hyperparameter » Prompt » Prompting
