activity
20242026
most citedGeAR: Graph-enhanced Agent for Retrieval-augmented Generation

1 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2026

How broad is that claim? Mapping Generalisation in NLP Research

Chenxin Diao, Nataliya Stepanova, Emily Allaway

Generalisations are common in scientific communication, even though they are semantically ambiguous. An automated method is needed to identify and categorise claims according to th…

cs.CL2025

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…

cs.HC2025

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

Kaixuan Wang, Jason T. Jacques, Chenxin Diao +1

Access to accurate and actionable harm reduction information can directly impact the health outcomes of People Who Use Drugs (PWUD), yet existing online channels often fail to meet…

cs.CL2025

HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs

Kaixuan Wang, Chenxin Diao, Jason T. Jacques +2

Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy provides non-judgemental, evidence-based information in…

cs.CL2024★ 1 cited

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.…

cs.AI2024

From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle

Kaustubh Vyas, Damien Graux, Yijun Yang +8

In response to the call for agent-based solutions that leverage the ever-increasing capabilities of the deep models' ecosystem, we introduce Hive -- a comprehensive solution for kn…

cs.CL2024★ 1 cited

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…