3 accepted papers
Rationale learning aims to automatically uncover the underlying explanations for NLP predictions. Previous studies in rationale learning mainly focus on the relevance of independent tokens with the predictions without considering their marginal contribution and the collective readability of extracte…
Retrieval-Augmented Generation (RAG) systems combine external data retrieval with text generation and have become essential in applications requiring accurate and context-specific responses. However, their reliance on external data raises critical concerns about unauthorized collection and usage of…
Human annotation is costly and impractical when it comes to scarcely labeled data. Besides, the presence of biased language in well-known benchmarks notably misleads predictive models to perform incredibly well, not because of the model capability but due to the hidden false correlations in the ling…