6 citations · 9 across the 7 of their papers we have counts for
7 papers
SubSearch: Intermediate Rewards for Unsupervised Guided Reasoning in Complex Retrieval
Roxana Petcu, Evangelos Kanoulas, Maarten de Rijke
Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often require multi-step reasoning o…
Query Decomposition for RAG: Balancing Exploration-Exploitation
Roxana Petcu, Kenton Murray, Daniel Khashabi +4
Retrieval-augmented generation (RAG) systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggrega…
A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers
Roxana Petcu, Samarth Bhargav, Maarten de Rijke +1
Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they still…
Leveraging Graph Structures to Detect Hallucinations in Large Language Models
Noa Nonkes, Sergei Agaronian, Evangelos Kanoulas +1
Large language models are extensively applied across a wide range of tasks, such as customer support, content creation, educational tutoring, and providing financial guidance. Howe…
A Survey on Recent Advances in Conversational Data Generation
Heydar Soudani, Roxana Petcu, Evangelos Kanoulas +1
Recent advancements in conversational systems have significantly enhanced human-machine interactions across various domains. However, training these systems is challenging due to t…
Efficient data selection employing Semantic Similarity-based Graph Structures for model training
Roxana Petcu, Subhadeep Maji
Recent developments in natural language processing (NLP) have highlighted the need for substantial amounts of data for models to capture textual information accurately. This raises…
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…