Editing Openai/695ac389-b450-800e-a8b3-540cf70d0ecc (section)

===== 1. ''“Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models”'' =====
This very recent (Nov 21 2025) paper formalizes the idea that an LLM should abstain from answering when it lacks reliable knowledge, rather than hallucinating a response. It introduces Aspect-Based Causal Abstention (ABCA), a framework that assesses internal diversity of model knowledge and enables the LLM to choose to output an abstention (like “I don’t know”) when appropriate—improving reliability and interpretability compared to previous methods. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/abs/2511.17170|publisher=arxiv.org|access-date=2026-01-05}}</ref>
# ''“Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models”''
This research paper defines Abstention Ability (AA) for LLMs, which is exactly the capability to withhold an answer when the model is uncertain or lacks enough information. It highlights that traditional metrics and models don’t reliably measure or perform this behavior, and evaluates models’ abilities to use answers like “I don’t know” correctly across test cases. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/abs/2407.16221|publisher=arxiv.org|access-date=2026-01-05}}</ref>
# ''“R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’”''
This earlier work (Nov 2023) presents a method to train LLMs so that they explicitly learn to refuse to answer questions that fall outside their knowledge, rather than generate a hallucinated answer. This is done through a special “refusal-aware instruction tuning” process. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/abs/2311.09677|publisher=arxiv.org|access-date=2026-01-05}}</ref>