Summary of I Am a Strange Dataset: Metalinguistic Tests For Language Models, by Tristan Thrush et al.
I am a Strange Dataset: Metalinguistic Tests for Language Models
by Tristan Thrush, Jared Moore, Miguel Monares, Christopher Potts, Douwe Kiela
First submitted to arxiv on: 10 Jan 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 Medium Difficulty summary: This paper introduces “I am a Strange Dataset”, a new dataset designed to test large language models’ (LLMs) ability to handle metalinguistic self-reference statements. The dataset consists of two subtasks: generation, where LLMs continue statements like “The penultimate word in this sentence is”, and verification, where LLMs judge the truth of statements like “The penultimate word in this sentence is sentence.” (false). The authors also provide minimally different metalinguistic non-self-reference examples to probe models’ ability to handle metalinguistic language. To validate the dataset, they test various open-source and closed-source LLMs with parameters ranging from 7B to 70B, finding that even large-scale models struggle to perform above chance on both subtasks. Notably, GPT-4 is the only model to consistently outperform chance, but still achieves only around 60% accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper creates a special kind of dataset called “I am a Strange Dataset” that tests how well computers can understand sentences about themselves and language. The dataset has two parts: one where computers try to continue a sentence in the right way, and another where they have to say whether a statement is true or false. The authors also included some simpler examples to see if computers can handle this type of language at all. They tested different computer models on this dataset and found that even very smart ones have trouble doing well on these tasks. One model called GPT-4 did better than the others, but still wasn’t great. Humans were much better at understanding these sentences. |
Keywords
* Artificial intelligence * Gpt
