8 citations · 13 across the 12 of their papers we have counts for
13 papers
Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
Wenyu Huang, Xinyu Hou, Pavlos Vougiouklis +2
Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning with Verifiable Rewards (RLVR)…
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…
An Extensive Evaluation of PDDL Capabilities in off-the-shelf LLMs
Kaustubh Vyas, Damien Graux, Sébastien Montella +5
In recent advancements, large language models (LLMs) have exhibited proficiency in code generation and chain-of-thought reasoning, laying the groundwork for tackling automatic form…
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.…
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…
How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency
Danna Zheng, Mirella Lapata, Jeff Z. Pan
Large Language Models (LLMs) are increasingly explored as knowledge bases (KBs), yet current evaluation methods focus too narrowly on knowledge retention, overlooking other crucial…
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…
Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts
Wenyu Huang, Guancheng Zhou, Mirella Lapata +3
Although Large Language Models (LLMs) are effective in performing various NLP tasks, they still struggle to handle tasks that require extensive, real-world knowledge, especially wh…
Archer: A Human-Labeled Text-to-SQL Dataset with Arithmetic, Commonsense and Hypothetical Reasoning
Danna Zheng, Mirella Lapata, Jeff Z. Pan
We present Archer, a challenging bilingual text-to-SQL dataset specific to complex reasoning, including arithmetic, commonsense and hypothetical reasoning. It contains 1,042 Englis…
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…
TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness
Danna Zheng, Danyang Liu, Mirella Lapata +1
Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications. However, concerns have arisen rega…
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
Hongru Wang, Wenyu Huang, Yang Deng +6
Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-…