Summary of Panda: Preference Adaptation For Enhancing Domain-specific Abilities Of Llms, by An Liu et al.
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
by An Liu, Zonghan Yang, Zhenhe Zhang, Qingyuan Hu, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Preference Adaptation for Enhancing Domain-specific Abilities of Large Language Models (PANDA) method enhances domain-specific capabilities without requiring fine-tuning. By leveraging response preferences from expert models, PANDA improves text classification and interactive decision tasks performance in LLMs. This tuning-free approach even outperforms the expert model on 4 ScienceWorld tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand language, but they’re not perfect. Sometimes, they don’t do as well as special models made just for specific jobs. To help LLMs get better at these jobs, scientists came up with a new way to make them smarter without needing to retrain the entire model. This new method is called PANDA and it looks at how other expert models make decisions to improve LLM performance. PANDA makes LLMs really good at classifying text and making choices, even beating some of those special expert models! |
Keywords
* Artificial intelligence * Fine tuning * Text classification
