Large Language Models (LLMs) have shown impressive performance in classification tasks but struggle when correct labels are absent. This limitation raises concerns about their comprehension and intelligence. Two primary concerns in LLMs are versatility and label processing, as well as discriminative vs. generative capabilities. To address these concerns, a set of benchmarks called KNOW-NO has been introduced, including tasks like BANK77, MC-TEST, and EQUINFER. A new metric called OMNIACCURACY has also been presented to evaluate LLM performance more accurately. The research highlights the limitations of LLMs when correct answers are missing, introduces the CLASSIFY-W/O-GOLD framework, and provides a comprehensive assessment of LLM capabilities in different classification scenarios. Overall, the study aims to improve understanding of LLM performance in classification tasks and provide a more nuanced evaluation of their capabilities.
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Source link: https://www.marktechpost.com/2024/07/02/understanding-the-limitations-of-large-language-models-llms-new-benchmarks-and-metrics-for-classification-tasks/?amp
New benchmarks and metrics for classification tasks with LLMs. #Limitations
