Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
Inside-out: Hidden factual knowledge in llms
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
DECOR introduces a theory-grounded multi-agent system that decomposes contexts into atomic units, scores four manipulation dimensions per unit, and aggregates profiles into a global deception index, reporting SOTA results on single- and multi-turn benchmarks.
LLMs need metacognition to align expressed uncertainty with their actual knowledge boundaries, moving beyond knowledge expansion to reduce confident errors.
Adding controlled noise and irrelevant persona contexts across training and testing stages for strong LLMs yields better reasoning and efficiency than high-quality data alone, reaching 76.7% on AIME24/25 with Qwen2.5-32B.
A framework for inference-time knowledge graph construction and expansion improves factual accuracy in LLMs on three QA benchmarks by combining internal LLM knowledge with selective external retrieval.
citing papers explorer
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Cross-Lingual Exploration for Parametric Knowledge
Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
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DECOR: Auditing LLM Deception via Information Manipulation Theory
DECOR introduces a theory-grounded multi-agent system that decomposes contexts into atomic units, scores four manipulation dimensions per unit, and aggregates profiles into a global deception index, reporting SOTA results on single- and multi-turn benchmarks.
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Hallucinations Undermine Trust; Metacognition is a Way Forward
LLMs need metacognition to align expressed uncertainty with their actual knowledge boundaries, moving beyond knowledge expansion to reduce confident errors.
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Input-Time Scaling: Adding Noise and Irrelevance into Less-Is-More Drastically Improves Reasoning Performance and Efficiency
Adding controlled noise and irrelevant persona contexts across training and testing stages for strong LLMs yields better reasoning and efficiency than high-quality data alone, reaching 76.7% on AIME24/25 with Qwen2.5-32B.
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Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
A framework for inference-time knowledge graph construction and expansion improves factual accuracy in LLMs on three QA benchmarks by combining internal LLM knowledge with selective external retrieval.