arXiv:2608.17809v2 Announce Type: replace
Abstract: Humans naturally form and express beliefs in daily communication, e.g., "I think the answer is 3" or "I suppose that's right." Such beliefs inevita...
By Quang Minh Nguyen, Luis Frentzen Salim
arXiv:2607. 19355v1 Announce Type: new Abstract: LLMs are increasingly used with external knowledge sources like the internet.
By Joshua Ashkinaze, Laura Kurek, Alina Faisal, Tongyuan Miao, Mariam Joseph, Ceren Budak, Eric Gilbert
CARGO is a framework for evaluating agentic AI systems in production that addresses the problem of reference-instance divergence (RID), where reference-based judges penalize correct answers that involve different entity identifiers. It treats retrieved references as procedural exemplars, grounds judgments in the live instance’s context, assigns a three-way status to claims, and gates evaluation by retrieval confidence. Using the CARGO-Bench diagnostic suite, CARGO eliminates false penalties and improves discrimination while revealing a limitation in detecting procedural corruptions.
By Mukul Chhabra, Shail Patel, Luigi Medrano
arXiv:2606. 20245v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt.
By Huang Peng, Jiuyang Tang, Weixin Zeng, Hao Xu, Xiang Zhao
arXiv:2608.30987v1 Announce Type: cross
Abstract: Supervised fine-tuning (SFT) trains a base language model to imitate target responses, and these targets may require knowledge the base model has not...
By Arthur Becker, Jakob Kemmler, David Thulke, Christine Sch\"afer, Christian Dugast, Hermann Ney
The paper introduces a taxonomy of six types of contextual knowledge conflicts—factual, inferential, temporal, granularity, perspective, and ambiguity—and presents the ContextConflict dataset with 5,781 samples covering reasoning and summarization tasks. Experiments on nine large language models reveal that current models struggle to resolve these conflicts, exhibit a bias toward earlier evidence, and show latent awareness of conflicts in their internal representations. The authors propose a training‑free, label‑free steering method that adjusts activations to better incorporate evidence, consistently improving reasoning accuracy and producing higher‑quality, balanced summaries on the dataset.
By Xinye Yang, Zhenyang Liu, Ruisi Li, Yuanyuan Lei