The research team at Apppley reports that the mathematical testing of Large Language Models (LLMs) may not be sufficient. For instance, when testing the reasoning capabilities of new LLM models using the GSM8K test, the scores consistently improve. However, when slightly modifying the test questions, many models perform significantly worse, indicating that these models may struggle to truly understand the questions posed.
The team’s approach is to create the GSM-Symbolic test set as a template for diverse test creation. Subsequently, testing is conducted with varying inputs. The results show that many models perform poorly when numerical values are altered, while changes like different names still yield accurate results. Additionally, adding irrelevant text in the GSM-NoOps test set also leads to decreased performance.
The efficacy of LLMs in GSM-Symbolic varies, with GPT-4o showing the least decline. However, increasing the complexity of tasks by adding text to the questions results in a clear decrease in scores across all models, including GPT-4o.
TLDR: Research suggests that Large Language Models face challenges in mathematical testing, with modifications impacting their performance differently. GPT-4o shows the least decline in performance, but all models struggle with increased task complexity.
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