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LLM judges struggle with incomplete records for document verification

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates the reliability of Large Language Model (LLM) judges in verifying document currency. Researchers found that while LLMs are generally effective at identifying replaced citations when provided with a complete amendment record, their performance significantly drops when record gaps exist. In tests where links were removed from the record, models like Gemini 3.1 Pro and GPT-5.5 performed much better using only the document texts than with the incomplete record. The study suggests that providing LLMs with incomplete or unverified records can lead to missed citations and false alarms, impacting their accuracy in verifying document currency. AI

IMPACT Highlights limitations in LLM's ability to verify information accuracy when faced with incomplete data, impacting their reliability in critical applications.

RANK_REASON Research paper published on arXiv detailing LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

LLM judges struggle with incomplete records for document verification

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Research paper published on arXiv detailing LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Unnati Shah, Samruddhi Kakade, Aum Patel ·

    What Do LLM Judges Actually Verify? Matched Controls and Record Gaps for Document Currency

    arXiv:2611.01311v1 Announce Type: cross Abstract: LLM judges often check, against an amendment record, whether the documents an answer cites are still in force. Such records are rarely complete. From a table of replacements, our test generates matched answers citing a replaced do…

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