Summary of Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently, by Kanishka Misra et al.
Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently
by Kanishka Misra, Allyson Ettinger, Kyle Mahowald
First submitted to arxiv on: 12 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 This study investigates how language models (LMs) perform in meaning-sensitive tasks when provided with experimental contexts. Recent evaluations have shown that LMs can excel in these tasks when given in-context examples and instructions, but it’s unclear if this improvement translates to other tasks. The researchers focus on property inheritance, where LMs predict semantic properties of novel concepts. They find that in-context examples and instructions do improve LMs’ performance, but only up to a point. Some LMs even adopt shallow, non-semantic heuristics from their inputs, suggesting that the underlying computational principles for semantic property inference are still not well understood. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how language models (LMs) do on certain tasks when given special help. These tasks involve understanding meanings and can be tricky. The researchers want to know if giving LMs examples and instructions makes a difference. They test this with a task called property inheritance, where LMs try to figure out new concepts based on what they already know. Surprisingly, the extra help does make a big difference! But only some LMs get better, while others just use shortcuts instead of really understanding. |
Keywords
» Artificial intelligence » Inference
