5 papers
Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Jia-Huei Ju, Suzan Verberne, Maarten de Rijke +1
Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources. While the effectiveness of the retrieval mod…
Generative Retrieval with Few-shot Indexing
Arian Askari, Chuan Meng, Mohammad Aliannejadi +3
Existing generative retrieval (GR) methods rely on training-based indexing, which fine-tunes a model to memorise associations between queries and the document identifiers (docids)…
Multi-Step Reasoning with Large Language Models, a Survey
Aske Plaat, Annie Wong, Suzan Verberne +3
Large language models (LLMs) with billions of parameters exhibit in-context learning abilities, enabling few-shot learning on tasks that the model was not specifically trained for.…
Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models
Amin Abolghasemi, Leif Azzopardi, Seyyed Hadi Hashemi +2
Attributing answers to source documents is an approach used to enhance the verifiability of a model's output in retrieval augmented generation (RAG). Prior work has mainly focused…
Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs
Arian Askari, Roxana Petcu, Chuan Meng +4
Identifying user intents in information-seeking dialogs is crucial for a system to meet user's information needs. Intent prediction (IP) is challenging and demands sufficient dialo…