Summary of Contests: a Framework For Consistency Testing Of Span Probabilities in Language Models, by Eitan Wagner et al.
CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models
by Eitan Wagner, Yuli Slavutsky, Omri Abend
First submitted to arxiv on: 30 Sep 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 The abstract discusses the reliability of language models as probability estimators, focusing on the consistency of scores across different assignments of joint probabilities to word spans. The authors introduce a novel framework called ConTestS (Consistency Testing over Spans) that uses statistical tests to assess score consistency. They conduct experiments on real and synthetic data, finding that both Masked Language Models (MLMs) and autoregressive models exhibit inconsistent predictions. Autoregressive models show larger discrepancies, while MLMs tend to produce more consistent predictions for larger models. The analysis also reveals that prediction entropies can provide insights into the true word span likelihood, which can aid in selecting optimal decoding strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are used to predict words based on context, but how reliable are these predictions? Researchers found that language models don’t always agree with themselves when predicting words. They tested different ways of combining information and found that some models were more consistent than others. This matters because it can help us make better choices about which model to use for a particular task. The researchers also found that looking at how sure the model is about its prediction (its “entropy”) can give us clues about whether it’s making a good guess or not. |
Keywords
» Artificial intelligence » Autoregressive » Likelihood » Probability » Synthetic data
