Summary of Fine-tuning and Prompt Optimization: Two Great Steps That Work Better Together, by Dilara Soylu et al.
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
by Dilara Soylu, Christopher Potts, Omar Khattab
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 abstract describes a challenge in optimizing complex Natural Language Processing (NLP) systems, which typically involve modular pipelines with distinct language models and prompt templates. The authors propose combining two optimization strategies to maximize a downstream task metric: optimizing both the module-level language model weights and the associated prompt templates. This approach, dubbed “BetterTogether,” is tested on various tasks using different large language models (LLMs) like Mistral-7b, LLaMA-2-7b, and LLaMA-3-8b. The results show that BetterTogether outperforms direct optimization of weights alone or prompts alone by up to 60% and 6%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make complex language models better at tasks like question-answering, math problems, and recognizing patterns in text. It does this by making the model learn from itself using two new ideas: changing its own “weights” (like a puzzle piece) and adjusting what it’s trying to do based on how well it’s doing. This helps the model get better at tasks like answering questions that require looking up information in multiple places or solving math problems. |
Keywords
» Artificial intelligence » Language model » Llama » Natural language processing » Nlp » Optimization » Prompt » Question answering
