Learning to Verify Summary Facts with Fine-Grained LLM Feedback

Training automatic summary fact verifiers often faces the challenge of a lack of human-labeled data. In this paper, we explore alternative way of leveraging Large Language Model (LLM) generated feedback to address the inherent limitation of using human-labeled data. We introduce FineSumFact, a large-scale dataset containing fine-grained factual feedback on summaries. We employ 10 distinct LLMs for diverse summary generation and Llama-3-70B-Instruct for feedback. We utilize this dataset to fine-tune the lightweight open-source model Llama-3-8B-Instruct, optimizing resource efficiency while maintaining high performance. Our experimental results reveal that the model trained on extensive LLM-generated datasets surpasses that trained on smaller human-annotated datasets when evaluated using human-generated test sets. Fine-tuning fact verification models with LLM feedback can be more effective and cost-efficient than using human feedback. The dataset is available at https://github.com/DISL-Lab/FineSumFact.

DM: Dual-path Magnitude Network for General Speech Restoration

In this paper, we introduce a novel general speech restoration model: the Dual-path Magnitude (DM) network, designed to address multiple distortions including noise, reverberation, and bandwidth degradation effectively. The DM network employs dual parallel magnitude decoders that share parameters: one uses a masking-based algorithm for distortion removal and the other employs a mapping-based approach for speech restoration. A novel aspect of the DM network is the integration of the magnitude spectrogram output from the masking decoder into the mapping decoder through a skip connection, enhancing the overall restoration capability. This integrated approach overcomes the inherent limitations observed in previous models, as detailed in a step-by-step analysis. The experimental results demonstrate that the DM network outperforms other baseline models in the comprehensive aspect of general speech restoration, achieving substantial restoration with fewer parameters.