Large Language Models Obscure Scholarly Idea Provenance
A Correspondence in Nature Machine Intelligence warns that large language models (LLMs), trained on vast scholarly corpora, make source provenance opaque and often conceal which researchers influenced generated text. The authors argue this loss of provenance can deprive scholars of credit and undermine academic priority norms that favor published firsts over informal contributions. They call for improved interpretability and provenance-tracking in research tools and publishing practices.
Key Points
- 1Demonstrates LLM-generated texts obscure which scholarly sources influenced specific outputs
- 2Argues lost provenance deprives researchers of credit and distorts scholarly priority systems
- 3Implies need for interpretability and traceability tools to attribute ideas within LLMs and publications
Scoring Rationale
Highlights credible, peer-acknowledged provenance concern with broad academic implications, but offers limited concrete solutions or implementations
Sources
Public references used for this report.
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