Search Results for author:
Found 4 papers, 2 papers with code
FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
With the above challenges in mind, in this paper, we propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models (e. g., ChatGPT).
FELM: Benchmarking Factuality Evaluation of Large Language Models
In this benchmark, we collect responses generated from LLMs and annotate factuality labels in a fine-grained manner.
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings
In summary, our results confirm the effectiveness of our proposed model for the AVSS task with proper fine-tuning strategies, demonstrating that multi-modal self-supervised embeddings obtained from AV-HuBERT can be generalized to audio-visual regression tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information.
