论文标题
改进的梁搜索抽象摘要中缓解幻觉的缓解
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization
论文作者
论文摘要
大型语言模型的进步大大提高了其在有条件的语言生成任务（包括幻觉的摘要）方面的表现。为了减少幻觉，常规方法提出了改进光束搜索或使用事实检查器作为后处理步骤。在本文中，我们调查了自然语言推断（NLI）的使用需要指标来检测和防止汇总产生的幻觉。我们通过计算在显着增强的贪婪解码过程中计算输入上下文和摘要模型生成的光束之间的概率分数，提出了NLI辅助的重新排列机制。此外，引入了多样性指标，以比较其与香草束搜索的有效性。我们提出的算法显着优于XSUM和CNN/DM数据集上的香草束解码。
Advancement in large pretrained language models has significantly improved their performance for conditional language generation tasks including summarization albeit with hallucinations. To reduce hallucinations, conventional methods proposed improving beam search or using a fact checker as a postprocessing step. In this paper, we investigate the use of the Natural Language Inference (NLI) entailment metric to detect and prevent hallucinations in summary generation. We propose an NLI-assisted beam re-ranking mechanism by computing entailment probability scores between the input context and summarization model-generated beams during saliency-enhanced greedy decoding. Moreover, a diversity metric is introduced to compare its effectiveness against vanilla beam search. Our proposed algorithm significantly outperforms vanilla beam decoding on XSum and CNN/DM datasets.
