arXiv:2602. 02470v2 Announce Type: replace Abstract: Autoregressive large language models (LLMs) have achieved remarkable success in many complex tasks, yet they can still fail in very simple logical reasoning such as the "reversal curse" -- when trained on forward knowledge data of the form "$A \rightarrow B$" (e.
By Xutao Ma, Yixiao Huang, Hanlin Zhu, Somayeh Sojoudi
arXiv:2608.29109v1 Announce Type: new
Abstract: Large language models often answer structurally unanswerable questions, such as computing cot(-540{\deg}) or evaluating (1).startswith("1"), instead of...
By Yucheng Du, Xiyang Hu
arXiv:2507. 01335v4 Announce Type: replace-cross Abstract: Autoregressive language models are trained exclusively left-to-right.
By Xunjian Yin, Sitao Cheng, Yuxi Xie, Xinyu Hu, Li Lin, Xinyi Wang, Liangming Pan, William Yang Wang, Xiaojun Wan
Enterprise Document Intelligence [Vol. 1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable The post Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination appeared first on Towards Data Science .
By Kezhan Shi
arXiv:2608. 03512v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance on text-to-knowledge graph generation and related tasks.
By Sefika Efeoglu, Adrian Paschke
The article discusses the importance of a Retrieval-Augmented Generation (RAG) system providing clear evidence when it states that information is not present in a document. It outlines four distinct types of evidence that should accompany such a claim to avoid presenting a confident but incorrect answer or an unsupported “no answer.” The piece emphasizes that each evidence type serves as a safeguard against misinformation in enterprise document intelligence.
By Kezhan Shi