Summary of Using Combinatorial Optimization to Design a High Quality Llm Solution, by Samuel Ackerman et al.
Using Combinatorial Optimization to Design a High quality LLM Solution
by Samuel Ackerman, Eitan Farchi, Rami Katan, Orna Raz
First submitted to arxiv on: 15 May 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 This novel LLM-based solution design approach utilizes combinatorial optimization and sampling to create high-quality solutions. The method identifies key factors influencing solution quality, including prompt types, LLM inputs, and parameters governing generation and design alternatives. By understanding these factors, subject matter expert knowledge can be infused into the process. Combinatorial optimization is then used to create a subset of alternatives that ensures all desired interactions occur, which are subsequently developed into benchmarks. The approach accelerates the design and evaluation of each benchmark, making it suitable for scenarios where manual steps and human evaluation are time-consuming. As a baseline, this method can also be used to compare and validate autoML approaches that search over factors governing solution quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to create good solutions using language models (LLMs). The approach uses math and sampling to find the right combination of inputs that makes a high-quality solution. It does this by identifying important factors, such as what kind of prompts to use and how to design alternatives. By understanding these factors, experts can add their knowledge to the process. This approach is fast and efficient, making it useful when designing and evaluating each alternative takes a lot of time and effort. |
Keywords
» Artificial intelligence » Optimization » Prompt
