In this paper, we introduce Transform Retrieval, a novel approach aimed at improving Textual Entailment Retrieval within the framework of Retrieval-Augmented Generation (RAG). While RAG has shown promise in enhancing Large Language Models by retrieving relevant documents to extract specific knowledge or mitigate hallucination, current retrieval methods often prioritize relevance without ensuring the retrieved documents semantically support answering the queries. Transform Retrieval addresses this gap by transforming query embeddings to better align with semantic entailment without re-encoding the document corpus. We achieve this by using a transform model and employing a contrastive learning strategy to optimize the alignment between transformed query embeddings and document embeddings for better entailment.We evaluated the framework using BERT as frozen pre-trained encoder and compared it with a fully fine-tuned skyline model. Experimental results show that Transform Retrieval with simple MLP consistently approaches the skyline across multiple datasets, demonstrating the method’s effectiveness. The high performance on HotpotQA highlights its strength in many-to-many retrieval scenarios.

It is imperative to ensure the stability of every prediction made by a language model; that is, a language’s prediction should remain consistent despite minor input variations, like word substitutions. In this paper, we investigate the problem of certifying a language model’s robustness against Universal Text Perturbations (UTPs), which have been widely used in universal adversarial attacks and backdoor attacks. Existing certified robustness based on random smoothing has shown considerable promise in certifying the input-specific text perturbations (ISTPs), operating under the assumption that any random alteration of a sample’s clean or adversarial words would negate the impact of sample-wise perturbations. However, with UTPs, masking only the adversarial words can eliminate the attack. A naive method is to simply increase the masking ratio and the likelihood of masking attack tokens, but it leads to a significant reduction in both certified accuracy and the certified radius due to input corruption by extensive masking. To solve this challenge, we introduce a novel approach, the superior prompt search method, designed to identify a superior prompt that maintains higher certified accuracy under extensive masking. Additionally, we theoretically motivate why ensembles are a particularly suitable choice as base prompts for random smoothing. The method is denoted by superior prompt ensembling technique. We also empirically confirm this technique, obtaining state-of-the-art results in multiple settings. These methodologies, for the first time, enable high certified accuracy against both UTPs and ISTPs. The source code of CR-UTP is available at https://github.com/UCF-ML-Research/CR-UTP.