arXivPaperNeeds Review
An Exploratory Evaluation of LLM-Assisted Rewriting of Moderate-Complexity Financial Sentences for DisCoCat-Based Sentiment Analysis
Controlled LLM rewriting made harder financial sentences cheaper to process with DisCoCat, cutting circuit size by over 70%, but downstream accuracy improved only modestly.
arXiv
Source Summary
The workflow compresses, simplifies, or splits financial sentences before DisCoCat processing. Its strongest variants cut average qubit and gate counts by **more than 70%**; GPT-4.1-mini with Prompt B reached **0.550 ± 0.035** mean accuracy.
Practical Implication
Builders using agents as preprocessors should evaluate the transformed data against the downstream system, not just for linguistic fidelity. Prompt choice, filtering, and circuit cost all changed the result.
Agent-Ready Context
The workflow compresses, simplifies, or splits financial sentences before DisCoCat processing. Its strongest variants cut average qubit and gate counts by **more than 70%**; GPT-4.1-mini with Prompt B reached **0.550 ± 0.035** mean accuracy. Builders using agents as preprocessors should evaluate the transformed data against the downstream system, not just for linguistic fidelity. Prompt choice, filtering, and circuit cost all changed the result. The low-complexity baseline scored **0.521 ± 0.050**, so the observed accuracy gain was modest. Larger training splits were moderately associated with lower accuracy (**r=-0.446**), and the authors describe the findings as exploratory.
Context Map
contextdataresearch#prompting#context-engineeringUncertainty
The low-complexity baseline scored **0.521 ± 0.050**, so the observed accuracy gain was modest. Larger training splits were moderately associated with lower accuracy (**r=-0.446**), and the authors describe the findings as exploratory.