7 papers · 1 filter
OpenSIR: Open-Ended Self-Improving Reasoner
Wai-Chung Kwan, Joshua Ong Jun Leang, Pavlos Vougiouklis +3
Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models' ability to surpass…
Millions of -s: Extending GraphRAG to Millions of Documents
Zhili Shen, Chenxin Diao, Pascual Merita +2
Recent studies have explored graph-based approaches to retrieval-augmented generation, leveraging structured or semi-structured information -- such as entities and their relations…
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
Zhili Shen, Chenxin Diao, Pavlos Vougiouklis +12
Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios.…
Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation
Wenyu Huang, Pavlos Vougiouklis, Mirella Lapata +1
Multi-hop Question Answering (MHQA) adds layers of complexity to question answering, making it more challenging. When Language Models (LMs) are prompted with multiple search result…
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning
Zhili Shen, Pavlos Vougiouklis, Chenxin Diao +3
We focus on Text-to-SQL semantic parsing from the perspective of retrieval-augmented generation. Motivated by challenges related to the size of commercial database schemata and the…
A Usage-centric Take on Intent Understanding in E-Commerce
Wendi Zhou, Tianyi Li, Pavlos Vougiouklis +2
Identifying and understanding user intents is a pivotal task for E-Commerce. Despite its essential role in product recommendation and business user profiling analysis, intent under…