Beyond Questions: Evaluating the Stated Knowledge of LLMs
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arXiv:2605. 26937v2 Announce Type: replace-cross Abstract: Parametric knowledge in large language models (LLMs) is a cornerstone of their success, yet remains poorly understood.
arXiv:2504. 07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness.
arXiv:2606. 27047v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance across a wide range of tasks, but ensuring their reliability in highly technical domains remains a significant challenge.
arXiv:2509.21856v2 Announce Type: replace Abstract: Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains. How...
The paper introduces Knowledge-Weighted Fine‑Tuning, a method that estimates an instance‑level knowledge score through multi‑sampled inference and uses it to scale the learning signal. This approach encourages large language models to explicitly say "I don't know" on out‑of‑scope queries while preserving accuracy on known questions. The authors also propose new evaluation metrics for uncertainty, demonstrating that better discrimination between known and unknown instances improves overall performance.
arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.