Summary of Beyond Accuracy: Evaluating the Reasoning Behavior Of Large Language Models — a Survey, by Philipp Mondorf and Barbara Plank
Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models – A Survey
by Philipp Mondorf, Barbara Plank
First submitted to arxiv on: 2 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 Large language models have achieved impressive results on tasks requiring reasoning, sparking debate about their capacity for human-like reasoning. However, despite these successes, the extent of their reasoning abilities remains unclear. This uncertainty stems from focusing on task performance rather than a deeper analysis of the models’ reasoning behavior. This paper provides a comprehensive review of studies that move beyond accuracy metrics to offer insights into the models’ reasoning processes. Additionally, we survey methodologies for evaluating LLMs’ reasoning behavior, highlighting current trends and efforts towards more nuanced analyses. Our review suggests that LLLs rely on surface-level patterns and correlations in their training data rather than sophisticated reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and respond to human-like text. They’re really good at doing tasks that require thinking, like answering questions or writing short stories. But some people wonder if they can actually “think” like humans do. To figure this out, scientists have been studying how these models work and what makes them tick. One thing they’ve found is that the models often use shortcuts to get the answer right, rather than really understanding what’s going on. |
