The prevalence of fake news on social media calls for automated fact-checking systems that deliver not only accurate verdicts but also faithful explanations.However, existing large language model (LLM)-based methods often overlook deceptive misinformation styles in generated explanations, producing unfaithful rationales that may mislead human judgment.They also rely heavily on external knowledge sources, which can introduce hallucinations and incur substantial latency, undermining both reliability and responsiveness in realtime settings.To address these limitations, we propose REason-guided Fact-checking with Latent EXplanations (REFLEX), a selfrefining framework that explicitly controls reasoning style by anchoring explanations to the predicted verdict.REFLEX leverages selfdisagreement veracity signals between a backbone model and its fine-tuned variant to construct steering vectors, thereby naturally disentangling factual content from stylistic cues.Experiments on a real-world benchmark show that REFLEX achieves state-of-the-art performance with LLaMA-series models using only 465 self-refined samples.Owing to its transferability, REFLEX also yields gains of up to 7.54 Macro-F1 points on in-the-wild data.Further analysis shows that our method effectively mitigates faithful hallucination, leading to both more reliable explanations and more accurate verdicts than prior explainable fact-checking approaches.

The unparalleled performance of closedsourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT dialogues, as evidenced by Vicuna.However, due to challenges in gathering dialogues involving human participation, current endeavors like Baize and UltraChat rely on ChatGPT conducting roleplay to simulate humans based on instructions, resulting in overdependence on seeds, diminished human-likeness, limited topic diversity, and an absence of genuine multi-round conversational dynamics.To address the above issues, we propose a paradigm to simulate human behavior better and explore the benefits of incorporating more human-like questions in multiturn conversations.Specifically, we directly target human questions extracted from genuine human-machine conversations as a learning goal and provide a novel user simulator called 'Socratic'.The experimental results show our response model, 'PlatoLM', achieves SoTA performance among LLaMA-based 7B models in MT-Bench.Our findings further demonstrate that our method introduces highly human-like questioning patterns and rich topic structures, which can teach the response model better than previous works in multi-round conversations.