Accepted Papers

Retrieval-Augmented Generation (RAG) models are critically undermined by citation hallucinations, a deceptive failure where a model confidently cites a source that fails to support its claim. Existing work often attributes hallucination to a simple over-reliance on the model's parametric knowledge. ...

The quality of answers generated by large language models (LLMs) in retrieval-augmented generation (RAG) is largely influenced by the contextual information contained in the retrieved documents. A key challenge for improving RAG is to predict both the utility of retrieved documents---quantified as t...

Hallucinations in Large Language Model (LLM) outputs for Question Answering (QA) tasks critically undermine their real-world reliability. This paper introduces a methodology for robust, one-shot hallucination detection, specifically designed for scenarios with limited data access, such as interactin...

With the wide adaptation of Language Models for IR -- and specifically RAG systems -- the latency of the underlying LLM becomes a crucial bottleneck, since the long contexts of retrieved passages leads large prompts and therefore, compute increase. Prompt compression, which reduces the size of input...

Most conventional RAG pipelines rely on relevance-based retrieval, which often misaligns with utility --- that is, whether the retrieved passages actually improve generation quality. The limitations of existing utility-driven retrieval approaches for RAG are that, firstly, they are resource-intensiv...