Summary of Predict the Next Word: Humans Exhibit Uncertainty in This Task and Language Models _____, by Evgenia Ilia and Wilker Aziz
Predict the Next Word: Humans exhibit uncertainty in this task and language models _____
by Evgenia Ilia, Wilker Aziz
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 investigates the linguistic variability of language models (LMs), specifically assessing their ability to reproduce human-like responses in a next-word prediction task. The study focuses on three popular LMs: GPT2, BLOOM, and ChatGPT. By evaluating their performance at the word level using a dataset of alternative single-word continuations, researchers found that these models exhibit relatively low calibration to human uncertainty. This finding challenges the expected calibration error (ECE) metric, which is often used to evaluate LM performance. The study advises against relying on ECE in this setting and highlights the importance of developing more effective metrics for assessing LM variability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well language models can mimic human language patterns. It uses a special task called “next-word prediction” where the model has to choose the next word in a sentence based on what’s come before. The study focuses on three popular language models: GPT2, BLOOM, and ChatGPT. By testing these models, researchers found that they don’t always match human behavior. This means we need new ways to measure how well language models do this task. |
