1 citations · 1 across the 5 of their papers we have counts for
Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
KDCM: Reducing Hallucination in LLMs through Explicit Reasoning Structures
Jinbo Hao, Kai Yang, Qingzhen Su +2
To mitigate hallucinations in large language models (LLMs), we propose a framework that focuses on errors induced by prompts. Our method extends a chain-style knowledge distillatio…
cs.CL2026
Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning
Jinbo Hao, Kai Yang, Qingzhen Su +3
To address hallucination issues in large language models (LLMs), this paper proposes a method for mitigating prompt-induced hallucinations. Building on a knowledge distillation cha…
cs.CL2025
From Sequence to Structure: Uncovering Substructure Reasoning in Transformers
Xinnan Dai, Kai Yang, Jay Revolinsky +4
Recent studies suggest that large language models (LLMs) possess the capability to solve graph reasoning tasks. Notably, even when graph structures are embedded within textual desc…