Summary of Corrsynth — a Correlated Sampling Method For Diverse Dataset Generation From Llms, by Suhas S Kowshik et al.
CorrSynth – A Correlated Sampling Method for Diverse Dataset Generation from LLMs
by Suhas S Kowshik, Abhishek Divekar, Vijit Malik
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposes a new approach called CorrSynth for generating diverse datasets using zero-shot and few-shot prompting. Large language models (LLMs) have shown impressive performance in various tasks but suffer from limitations such as lack of diversity, prompt adherence, and potential biases. The authors tackle this challenge by introducing decoding-time guidance-based approaches that generate data faithful to the input prompt using a correlated sampling strategy. This method overcomes complexity drawbacks of other guidance-based techniques like classifier-based guidance. Extensive experiments demonstrate the effectiveness of CorrSynth, showcasing improvements in diversity and outperforming competitive baselines across four datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CorrSynth is a new way to generate data that’s more diverse and follows what you want it to say. Right now, big language models can do lots of things, but they sometimes make the same things over and over or don’t match what you asked for. This paper helps fix these problems by creating a system that generates data that’s both diverse and true to what you’re asking for. It works better than other methods and makes better datasets. |
Keywords
» Artificial intelligence » Few shot » Prompt » Prompting » Zero shot
