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OmniTrace framework enables source attribution in omni-modal LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed OmniTrace, a novel framework designed to attribute generated text in omni-modal large language models to specific parts of the input data. This method operates during the generation pass, utilizing late-layer attention to identify source units like text chunks, images, or audio/video segments without requiring additional forward passes or modality-specific tuning. OmniTrace demonstrates significant efficiency and accuracy, recovering a high percentage of attribution information compared to traditional leave-one-out methods and showing strong agreement with human-identified evidence. AI

IMPACT Enables more transparent and verifiable outputs from omni-modal LLMs, crucial for applications requiring evidence-based reasoning.

RANK_REASON The cluster contains an academic paper detailing a new framework for 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 →

OmniTrace framework enables source attribution in omni-modal LLMs

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The cluster contains an academic paper detailing a new framework for 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) · Qianqi Yan, Yichen Guo, Ching-Chen Kuo, Shan Jiang, Hang Yin, Yang Zhao, Xin Eric Wang ·

    OmniTrace: A Unified Framework for Generation-Time Source Attribution in Omni-Modal LLMs

    arXiv:2604.13073v2 Announce Type: replace-cross Abstract: An omni-modal language model can answer a question about a multimodal document, a recording, or a video, but it cannot show which part of the input the answer came from. Asking the model to cite its evidence yields what it…

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