English(EN)REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment
新研究解决大语言模型推理可靠性和幻觉问题
作者PulseAugur 编辑部·[10 个来源]·
多篇研究论文探讨了提高大语言模型（LLMs）在推理任务中的可靠性和准确性的方法。一种名为REIN的方法，通过反思和弃权来减少幻觉，允许模型自我纠正或承认无知。另一项研究调查了测试时概率聚合和逻辑表示编辑的有效性，发现在不同模型和任务上结果不一。此外，研究强调大语言模型在主观推理任务中存在困难，僵化的、以数学为中心的推理会降低性能，并提出通过上下文对齐的个性化来引导推理。其他工作考察了大语言模型如何在问题之间分配有限的计算资源，并研究了推理的内部图结构，表明其并非纯粹的顺序性。最后，研究探讨了监督微调和强化学习在回溯中的相互作用，以及掩码扩散语言模型在改进推理和早期退出方面的潜力。
AI
arXiv:2608.07931v1 Announce Type: new Abstract: Large reasoning models (LRMs) are prone to hallucination, which undermines their reliability and poses challenges for safe deployment. Hallucinations in LRMs arise from two distinct failure sources: reasoning hallucination, where fl…
arXiv:2608.08514v1 Announce Type: new Abstract: We independently reproduce two recent methods for making large language model (LLM) reasoning more reliable, and stress-test them across domains and models (RPC across four new task domains with Qwen3-8B, LCF across four 7-8B models…
arXiv:2608.08889v1 Announce Type: new Abstract: Recommendation systems thrive on personalization, where ''correctness'' is rarely a binary truth but a matter of subjective human preference. As Large Language Models (LLMs) are deployed as autonomous verifiers of safety and quality…
arXiv:2608.07968v1 Announce Type: cross Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one question at a time. Yet when multiple problems share an end-to-end cost or latency cons…
arXiv cs.AI
TIER_1English(EN)·Muhammad Ali Shafique, Kelly Marchisio·
arXiv:2608.08447v1 Announce Type: cross Abstract: Multilingual reasoning models are commonly evaluated by whether they arrive at the correct answer, but not by whether they preserve the intended language while reasoning and responding. This omission conceals important multilingua…
arXiv:2512.09636v3 Announce Type: replace Abstract: Mental-health reasoning with large language models (LLMs) is an evidence-constrained judgment problem: models must transform limited, subjective, and often ambiguous evidence into interpretations, decisions, or claims whose spec…
arXiv cs.CL
TIER_1English(EN)·Tianjun Zhong, Linyang He, Ziyang Li, Nima Mesgarani·
arXiv:2601.17593v3 Announce Type: replace Abstract: Recent progress in large language models has renewed interest in how multi-step reasoning is represented internally. While prior work often treats reasoning as a linear chain, many reasoning problems can be more naturally modele…
arXiv cs.CL
TIER_1English(EN)·Hexuan Wang, Yaxuan Ren, Srikar Bommireddypalli, Shuxian Chen, Adarsh Prabhudesai, Rongkun Zhou, Elina Baral, Philipp Koehn·
arXiv:2510.19990v2 Announce Type: replace Abstract: The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving. While current tooling for reasoning is built around next-token prediction trained mo…
