With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes
domains, ensuring the trustworthiness, safety, and observability of these systems has …

Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend
beyond the pre-trained knowledge of Large Language Models by augmenting the original …

Evaluating retrieval-augmented generation (RAG) presents challenges, particularly for
retrieval models within these systems. Traditional end-to-end evaluation methods are …

We introduce eCLIP, an enhanced version of the CLIP model that integrates expert
annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in …

Embedding-based retrieval serves as a dominant approach to candidate item matching for
industrial recommender systems. With the success of generative AI, generative retrieval has …

AI systems that serve natural language questions over databases promise to unlock
tremendous value. Such systems would allow users to leverage the powerful reasoning and …

Conversational recommendation (ConvRec) systems must understand rich and diverse
natural language (NL) expressions of user preferences and intents, often communicated in …

Large language models (LLMs) with long context windows have gained significant attention.
However, the KV cache, stored to avoid re-computation, becomes a bottleneck. Various …

Knowledge-based Visual Question Answering (KVQA) tasks require answering questions
about images using extensive background knowledge. Despite significant advancements …