Summary of Optimizing Llms with Direct Preferences: a Data Efficiency Perspective, by Pietro Bernardelle and Gianluca Demartini
Optimizing LLMs with Direct Preferences: A Data Efficiency Perspective
by Pietro Bernardelle, Gianluca Demartini
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 investigates the scalability, data efficiency, and effectiveness of Direct Preference Optimization (DPO) in fine-tuning pre-trained Large Language Models (LLMs). To achieve better alignment between LLMs and human preferences, the study compares model performance using varying percentages of a combined preference judgement dataset. The results show that increasing the amount of training data enhances and stabilizes model performance, while combining diverse datasets improves model effectiveness. Moreover, models trained with conversational prompts outperform those trained with question answering prompts when trained separately. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are used to generate text like humans do. To make them better at this, researchers want to teach them what people like and dislike. One way to do this is by using “preference data”, which is expensive to collect. This study looks at how different amounts of preference data affect the performance of LLMs. They found that the more data used, the better the models performed. Also, combining different types of data makes the models even better. When trained with conversational prompts, the models did best. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Optimization » Question answering
