Summary of Counterfactual Token Generation in Large Language Models, by Ivi Chatzi et al.
Counterfactual Token Generation in Large Language Models
by Ivi Chatzi, Nina Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, Manuel Gomez-Rodriguez
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 abstract presents an innovative approach to enhance state-of-the-art large language models with the ability to reason about counterfactual alternatives. This functionality allows them to generate tokens that consider what could have happened if a different choice was made in the past. The proposed causal model, built upon the Gumbel-Max structural causal model, enables any large language model to perform counterfactual token generation at a minimal cost and without requiring fine-tuning or prompt engineering. This technique is demonstrated on Llama 3 8B-Instruct and Ministral-8B-Instruct models, showcasing its effectiveness in generating coherent text. The study concludes by applying this technology to bias detection, revealing intriguing insights into the constructed world view of large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re reading a captivating story generated by a large language model. But what if the author had chosen a different character instead? This abstract explores ways to make these machines better at imagining alternative scenarios. The researchers develop a new way for language models to generate text that considers what would have happened if things went differently in the past. They test this approach on two popular models and show how it can be used to detect biases in the stories these models tell. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Llama » Prompt » Token
