Summary of Generalized Measures Of Anticipation and Responsivity in Online Language Processing, by Mario Giulianelli et al.
Generalized Measures of Anticipation and Responsivity in Online Language Processing
by Mario Giulianelli, Andreas Opedal, Ryan Cotterell
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 introduces a new framework for measuring predictive uncertainty in online language processing. It generalizes classic information-theoretic measures by simulating expected continuations of linguistic contexts. The framework provides formal definitions of anticipatory and responsive measures, allowing researchers to define new, more expressive measures beyond standard next-symbol entropy and surprisal. Empirical results show that using Monte Carlo simulation to estimate alternative responsive and anticipatory measures leads to enhanced predictive power for human cloze completion probability, ELAN, LAN, and N400 amplitudes, as well as greater complementarity with surprisal in predicting reading times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to measure how good language models are at guessing what comes next. It’s like trying to predict the next word in a sentence. The researchers created a new formula that can be used to estimate different types of uncertainty, which can help improve the accuracy of these predictions. They tested their formula and found that it works better than an older method called surprisal for predicting things like how long it takes to read a sentence. |
Keywords
» Artificial intelligence » Probability
