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This paper studies how multimodal claim verification models respond to stylistic text changes induced by LLM rewriting. Evaluating 11 open-weight VLMs (2B–38B), the authors find accuracy is largely robust to natural rewriting and controlled LLM-word injection, though hedging-oriented modifications cause consistent probability shifts across nearly all models.
This exploratory paper evaluates LLM-assisted rewriting of moderate-complexity financial sentences for DisCoCat-based sentiment analysis, finding that prompt-based compression can reduce circuit complexity by over 70% and slightly improve accuracy compared to a low-complexity baseline.