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Paper tackles data citation challenge for large language models

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the complex challenge of data citation for large language models (LLMs). The authors argue that LLMs' ability to mediate information access necessitates citation for credit and provenance, beyond simple verification. They propose three research directions: transforming influence estimates into references for training data, identifying datasets and query results at the correct granularity during inference, and defining references for knowledge graph facts to ensure credit propagation. Addressing these issues requires collaboration across database, information retrieval, knowledge representation, and artificial intelligence fields. AI

IMPACT This research could lead to more transparent and accountable LLM outputs by enabling proper attribution of training data.

RANK_REASON The cluster contains an academic paper discussing a novel research challenge in AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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

Paper tackles data citation challenge for large language models

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The cluster contains an academic paper discussing a novel research challenge in AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Gianmaria Silvello ·

    Data Citation for Large Language Models: A Challenge

    Large language models increasingly mediate access to information, and a growing body of work asks whether they cite the sources behind their outputs. That work treats citation as a verification device and applies it to textual documents. Scholarly citation serves two further func…

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