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LLMs exhibit CARLAR influence, hallucinating cheque amounts

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details a phenomenon called CARLAR influence, where multimodal large language models (LLMs) incorrectly extract the same amount for both courtesy and legal fields on cheques, even when annotations differ or are blank. The study evaluated GPT-4.1 and Claude-4.5 Sonnet across various visual prompting conditions, finding that GPT-4.1 achieved 73.7% CARLAR influence under courtesy redaction. Researchers also tested a compressed-image few-shot intervention, which improved Claude's joint extraction accuracy by 11.5 percentage points but had a mixed effect on GPT-4.1, highlighting the need for validation methods that consider visual context and inter-field dependencies. AI

IMPACT Highlights a specific hallucination risk in LLMs for financial document extraction, necessitating improved validation methods.

RANK_REASON Research paper published on arXiv detailing a specific hallucination risk in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs exhibit CARLAR influence, hallucinating cheque amounts

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Research paper published on arXiv detailing a specific hallucination risk in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Joong Ho (Joshua), Choi, Aditya Paul, Nidhi Singh ·

    CARLAR Influence: A Structured Hallucination Risk in Multimodal Cheque Extraction and an Exploratory Compressed-Image Countermeasure

    arXiv:2610.27192v1 Announce Type: cross Abstract: Multimodal large language models (LLMs) are increasingly used for structured extraction from financial documents, but their behavior under visual ambiguity is poorly understood. We study CARLAR (Courtesy Amount Recognition and Leg…

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