Summary of Customizing Language Model Responses with Contrastive In-context Learning, by Xiang Gao et al.
Customizing Language Model Responses with Contrastive In-Context Learning
by Xiang Gao, Kamalika Das
First submitted to arxiv on: 30 Jan 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 proposes an innovative approach to align large language models (LLMs) with human intent, particularly when generating content that meets specific criteria or tone. The method relies on contrastive examples, providing positive instances of intended output and negative examples that illustrate what characteristics to avoid. These negative examples can be sourced from labeled data, written by a human, or generated by the LLM itself. Before generating an answer, the model analyzes these examples to learn what to avoid, effectively articulating the user’s need. The proposed approach is tested on both synthetic and real-world datasets, including StackExchange and Reddit, showcasing significant performance improvements compared to standard few-shot prompting methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a computer how to write something that meets certain rules or tone. This paper shows how to do just that by giving the computer examples of what it should and shouldn’t say. The approach is simple: provide examples of good writing, then show the computer what’s bad. Before answering a question, the computer thinks about these examples to learn what not to do. This helps the computer understand what you want it to write. The paper tests this method on real-life data from websites like StackExchange and Reddit and finds that it works much better than usual. |
Keywords
» Artificial intelligence » Few shot » Prompting
