Summary of Auxiliary Task Demands Mask the Capabilities Of Smaller Language Models, by Jennifer Hu et al.
Auxiliary task demands mask the capabilities of smaller language models
by Jennifer Hu, Michael C. Frank
First submitted to arxiv on: 3 Apr 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 The paper presents findings on the relationship between task demands and language model (LM) performance, exploring whether evaluation methods with greater task demands can mask a model’s underlying abilities. The study investigates four tasks: analogical reasoning, reflective reasoning, word prediction, and grammaticality judgments, demonstrating that increased task demands result in lower performance for LMs with fewer parameters and less training data. These results suggest that LM performance should not be taken as an indicator of intelligence, but rather as a reflection of the model’s capacities influenced by researchers’ design choices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models (LMs) are like kids learning new things – they can do some tasks well, but struggle with others. The way we test them can affect how good they seem. The paper shows that if we make tests harder or more complicated, LMs will do worse. This is especially true for simpler models that don’t have as much information to work with. So, instead of just looking at how well an LM does on a test, we should consider the type of test and what it’s asking the model to do. |
Keywords
» Artificial intelligence » Language model » Mask
