Summary of Language Models with Conformal Factuality Guarantees, by Christopher Mohri et al.
Language Models with Conformal Factuality Guarantees
by Christopher Mohri, Tatsunori Hashimoto
First submitted to arxiv on: 15 Feb 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 research proposes a framework called conformal factuality to guarantee the correctness and factuality of language model outputs. By connecting language modeling with conformal prediction, the framework ensures high probability correctness guarantees for language models. The approach views the correctness of an output as an uncertainty quantification problem, where uncertainty sets are defined as the entailment set of the output. A back-off algorithm is used to provide high-probability correctness guarantees by making the output less specific and expanding associated uncertainty sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to ensure that language models produce accurate and factual outputs. The researchers developed a method called conformal factuality, which connects language modeling with a technique called conformal prediction. This connection allows for guaranteed correct outputs, even for complex tasks like question answering and math problems. The approach is simple, requiring only a few examples of human-annotated data. |
Keywords
* Artificial intelligence * Language model * Probability * Question answering
