LLMs Aid Quantum NLP in Financial Sentiment Analysis

Researchers have explored using Large Language Models (LLMs) to preprocess financial sentences for Quantum Natural Language Processing (QNLP) models, specifically the Distributional Compositional Categorical (DisCoCat) framework. This LLM-assisted rewriting aims to simplify complex sentences into formats compatible with DisCoCat, reducing computational costs. Experiments showed that GPT-4.1 mini with a specific prompt achieved the highest accuracy, outperforming a baseline that only used low-complexity sentences. The study suggests that LLM rewriting can enhance the usability of moderate-complexity inputs for DisCoCat, emphasizing the importance of prompt design and filtering for scalable QNLP applications in financial sentiment analysis. AI

IMPACT LLM-assisted rewriting shows potential to improve the efficiency and accuracy of quantum NLP models for financial sentiment analysis.

RANK_REASON The cluster contains an academic paper detailing an exploratory evaluation of LLM-assisted rewriting for QNLP-based sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• DisCoCat
• GPT-4.1 mini
• QNLP
• Stein et al. v. ‘Kathy K’ et al. (The Ship)

• paper
• other

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