Summary of Active Use Of Latent Constituency Representation in Both Humans and Large Language Models, by Wei Liu et al.
Active Use of Latent Constituency Representation in both Humans and Large Language Models
by Wei Liu, Ming Xiang, Nai Ding
First submitted to arxiv on: 28 May 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 paper explores how humans and large language models (LLMs) like ChatGPT process sentences internally, particularly in terms of hierarchical linguistic constituents. Classic theories suggest that the brain breaks down sentences into organized parts, whereas LLMs don’t explicitly parse these components. The authors investigate this by analyzing human and LLM behavior during a one-shot learning task where participants infer which words to delete from a sentence. Both humans and LLMs tend to remove constituent parts rather than random word strings. A naive model that only considers word properties and order doesn’t display this property. By examining deletion behaviors, the authors can reconstruct a latent constituency tree representation for both humans and LLMs, demonstrating that this representation can emerge in both. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to figure out how our brains and big language models like ChatGPT understand sentences. It’s like trying to solve a puzzle! Classic ideas say that our brain breaks down sentences into smaller parts. But these big language models don’t work the same way. Researchers looked at how people and these language models behave when they’re asked to remove words from a sentence. They found that both humans and language models tend to delete important parts of the sentence, rather than just picking random words. A simple model that only looks at word order didn’t do this. By studying what people and language models do, researchers can recreate how these models understand sentences. |
Keywords
» Artificial intelligence » One shot
