Reproduce “Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Dimitris Vamvourellis, Dhagash Mehta
Researchers at BlackRock investigated whether Chain-of-Thought (CoT) prompting benefits or hinders Large Language Models in financial sentiment analysis, finding that simpler, direct prompting consistently outperformed elaborate reasoning across various models. The study demonstrated that explicit reasoning can degrade alignment with human subjective judgments in such tasks.
Key insights
- For subjective tasks like financial sentiment analysis, prompting strategies should match the cognitive demands, favoring simpler approaches over elaborate reasoning.
- LLMs specifically optimized for reasoning may perform worse in intuitive tasks, indicating that built-in reasoning can be detrimental for subjective judgments.
- The study empirically demonstrates an "overthinking" phenomenon in LLMs, where increased reasoning verbosity correlates with decreased accuracy in subjective tasks.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.