Learning Shortcuts: On the Misleading Promise of NLU in Language Models
This is an incremental perspective on shortcut learning, highlighting a challenge for researchers and developers in accurately assessing and enhancing NLU in LLMs.
The paper addresses the problem of large language models (LLMs) using shortcuts in natural language understanding (NLU) tasks, which creates misleading performance gains and lacks generalizability, and it surveys research and urges more efforts to improve robustness and evaluation standards.
The advent of large language models (LLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found that LLMs often resort to shortcuts when performing tasks, creating an illusion of enhanced performance while lacking generalizability in their decision rules. This phenomenon introduces challenges in accurately assessing natural language understanding in LLMs. Our paper provides a concise survey of relevant research in this area and puts forth a perspective on the implications of shortcut learning in the evaluation of language models, specifically for NLU tasks. This paper urges more research efforts to be put towards deepening our comprehension of shortcut learning, contributing to the development of more robust language models, and raising the standards of NLU evaluation in real-world scenarios.