Summary of Learning Shortcuts: on the Misleading Promise Of Nlu in Language Models, by Geetanjali Bihani et al.
Learning Shortcuts: On the Misleading Promise of NLU in Language Models
by Geetanjali Bihani, Julia Taylor Rayz
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 abstract discusses the limitations of large language models (LLMs) in natural language processing. While LLMs have achieved significant performance gains, they often rely on shortcuts that create an illusion of enhanced performance without generalizability. This shortcut learning introduces challenges in accurately assessing natural language understanding in LLMs. The paper provides a survey of relevant research and explores the implications for NLU tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are great at processing language, but they might not be as smart as we think. Sometimes, they use shortcuts to make decisions instead of really understanding what’s being said. This can make them seem super smart, but it’s not a real sign of intelligence. The researchers in this paper want us to understand that LLMs are not always as good as they seem and that we need to do more research to improve their performance. |
Keywords
* Artificial intelligence * Language understanding * Natural language processing
