Summary of Reframing Data Value For Large Language Models Through the Lens Of Plausibility, by Mohamad Rida Rammal et al.
Reframing Data Value for Large Language Models Through the Lens of Plausibility
by Mohamad Rida Rammal, Ruida Zhou, Suhas Diggavi
First submitted to arxiv on: 30 Aug 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 This paper presents an alternative approach to data valuation for language models, shifting the focus from discriminative models to the plausibility of the data itself. The proposed method centers around the idea that data holds lesser value if it can be generated by the model with high probability. A novel value function is developed, grounded in intuitive criteria and derived from first principles with provable properties. The paper conducts a theoretical analysis of the value function and evaluates its performance across multiple scenarios and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps answer an important question: “How much is this data worth?” Right now, most methods for valuing data look at how useful it is for training models. But with bigger language models becoming more common, relying on these methods becomes too expensive and depends on specific techniques. The authors suggest a new way to think about data value – by looking at whether the model can plausibly generate the data itself. They create a special formula that’s easy to calculate and based on basic principles. The paper shows how this formula works and tests it with different scenarios and datasets. |
Keywords
» Artificial intelligence » Probability
