Summary of Prompt Baking, by Aman Bhargava et al.
Prompt Baking
by Aman Bhargava, Cameron Witkowski, Alexander Detkov, Matt Thomson
First submitted to arxiv on: 4 Sep 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 paper explores ways to modify the behavior of large language models (LLMs) by either prompting them or updating their weights. Prompting is a simple and effective method that specifies desired changes explicitly in natural language, whereas weight updates provide more expressive and permanent changes. The authors introduce a technique called “Prompt Baking” that converts prompts into new sets of weights for LLMs. This process minimizes the KL divergence between the original prompted LLM and the baked-in LLM’s probability distribution over token sequences. The paper presents various experiments, including baking chain-of-thought prompts to improve zero-shot performance on several benchmarks, baking news headlines to update an LLM’s knowledge, and baking instructions and personas to alleviate “prompt forgetting.” Additionally, the authors discuss implications for AI safety, continuous model updating, enhancing real-time learning capabilities in LLM-based agents, and generating more stable AI personas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make large language models (LLMs) better at doing certain tasks by giving them new instructions or changing their internal settings. The researchers found a way to take the instructions that make an LLM do something specific and turn them into permanent changes to the model’s behavior. This helps the model remember what it was told to do, even after being given new instructions later on. The paper also talks about how this technique can be used to improve AI models in general, making them safer and more useful. |
Keywords
» Artificial intelligence » Probability » Prompt » Prompting » Token » Zero shot
