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Decomposition-Enhanced Training for Post-Hoc Attributions In Language Models

arXiv — cs.CL•Thursday, October 30, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A new study introduces Decomposition-Enhanced Training for post-hoc attributions in large language models, addressing the challenges of reliable source attribution in long-document question answering. This advancement is crucial as it enhances trust in AI systems by improving their ability to synthesize information across multiple passages, making them more effective in complex reasoning tasks. As language models become more integrated into various applications, ensuring their reliability and transparency is essential for user confidence and broader adoption.
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