Summary of Kallini Et Al. (2024) Do Not Compare Impossible Languages with Constituency-based Ones, by Tim Hunter
Kallini et al. (2024) do not compare impossible languages with constituency-based ones
by Tim Hunter
First submitted to arxiv on: 16 Oct 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 This research investigates whether recent large language models (LLMs) can be considered computational devices that accurately describe all possible human languages, as posited by linguistic theory. The study examines LLMs’ ability to learn synthetic languages and finds that GPT-2 learns some more successfully than others, which is taken as evidence for the alignment of LLMs’ inductive biases with what is regarded as “possible” for human languages. However, this conclusion is unwarranted due to a significant confound. This paper delves into the confound and proposes approaches to construct a comparison that effectively tests the underlying issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at whether big language models can learn all possible human languages, like a computer program. Researchers tested GPT-2 on fake languages and found some were easier to learn than others. This might mean that these models have biases that match what we consider “real” languages. But there’s a problem with the way they did this test. In this paper, we’ll explore why it doesn’t work as planned. |
Keywords
» Artificial intelligence » Alignment » Gpt
