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This arXiv paper studies how false training data can influence a language model's factual answers and downstream decisions without any activation trigger, identifying an audit gap between direct probing of factual recall and the model's actual behavior, including a real-world case of bushfire arson misinformation on Facebook.
This paper introduces Hallucination-R1, a robustness-oriented paraphrase generation framework designed to identify and improve factual consistency in large language models by creating semantically equivalent yet challenging paraphrases.
The paper proposes a latent-space intervention method to improve cross-lingual factual consistency in large language models, achieving better alignment without loss in factual accuracy.
This paper proposes a multi-factor scoring system for evaluating LLM responses, integrating accuracy, conciseness, factual consistency, readability, and coherence. Applied to the TruthfulQA dataset, it reveals strengths and limitations of mainstream models, offering a transparent evaluation framework.
BinEval is a new framework that decomposes LLM evaluation criteria into atomic binary questions, improving interpretability and enabling targeted prompt optimization, achieving strong results on factual consistency benchmarks.
This paper introduces a method to improve factual consistency in text summarization by aggregating scores from multiple weak metrics via preference learning, achieving consistent factuality gains across various language models.