LLMs show promise in scientific text categorization with prompt chaining

Researchers have explored the use of Large Language Models (LLMs) for automatically categorizing scientific texts using prompt engineering techniques. Their study evaluated In-Context Learning (ICL) and Prompt Chaining against the ORKG taxonomy and the FORC dataset. Results indicate that Prompt Chaining significantly improves classification accuracy over pure ICL, outperforming older models like BERT for first and second-level classifications. However, LLMs still struggle with third-level topic classification, achieving only around 50% accuracy. AI

IMPACT Demonstrates prompt chaining's effectiveness for scientific text categorization, potentially improving research information retrieval systems.

RANK_REASON Academic paper evaluating LLM performance on a specific text classification task.

Read on arXiv cs.CL →

• arXiv
• BERT
• In-Context Learning
• FORC dataset
• Large Language Models
• Prompt Chaining

• paper
• other

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