Summary of The Fellowship Of the Llms: Multi-agent Workflows For Synthetic Preference Optimization Dataset Generation, by Samee Arif et al.
The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation
by Samee Arif, Sualeha Farid, Abdul Hameed Azeemi, Awais Athar, Agha Ali Raza
First submitted to arxiv on: 16 Aug 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 paper presents a novel approach to generating synthetic Preference Optimization (PO) datasets using multi-agent workflows. The methodology consists of two modules: response evaluation and response generation. In the response evaluation module, Large Language Models (LLMs) are used to automate the task typically performed by human annotators. The performance of LLMs as evaluators is assessed through three distinct prompting strategies, with GPT-4o-as-a-Judge showing consistency across all datasets. For the response generation module, different configurations of the LLM Feedback Loop are compared, with the LMM Feedback Loop, using Llama as the generator and Gemma as the reviewer, achieving a notable win rate. The paper’s findings contribute to automating and enhancing PO dataset generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to create synthetic Preference Optimization (PO) datasets. It does this by using many different Large Language Models (LLMs) working together. First, these LLMs help decide which responses are the best. Then, they work together to generate new responses. The paper shows that one specific combination of LLMs is better than others at doing this. By using this combination, computers can create PO datasets more efficiently and accurately. |
Keywords
» Artificial intelligence » Gpt » Llama » Optimization » Prompting
