Papers by Pavlos Vougiouklis
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning (2024.emnlp-main)
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| Challenge: | Existing methods for text-to-SQL semantic parsing are limited to retrieving schemata based on a single query. |
| Approach: | They propose a text-to-sql semantic parser that uses abstract syntax trees to select few-shot examples for retrieval-augmented generation. |
| Outcome: | The proposed model can generate approximated versions of SQL queries in parallel, and shows improvements over state-of-the-art benchmarks. |
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (L18-1)
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Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, Elena Simperl
| Challenge: | Existing datasets that provide alignments between natural language and knowledge bases (KB) triples are limited in size, lack coverage and are of unreported quality. |
| Approach: | They propose to build a large scale dataset of alignments between Wikipedia abstracts and Wikidata triples that is two orders of magnitude larger than the largest available alignments dataset. |
| Outcome: | The proposed dataset is two orders of magnitude larger than the largest available dataset and covers 2.5 times more predicates. |
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation (2025.findings-acl)
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Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Enting Chen, Damien Graux, Andre Melo, Ruofei Lai, Zeren Jiang, Zhongyang Li, Ye Qi, Yang Ren, Dandan Tu, Jeff Z. Pan
| Challenge: | Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers struggle with multi-hop retrieval scenarios. |
| Approach: | They propose a graph expansion mechanism that augments any conventional base retriever and an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. |
| Outcome: | The proposed system achieves state-of-the-art results on three multi-hop question answering datasets while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. |
Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA (2024.findings-emnlp)
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| Challenge: | Recent studies suggest that Knowledge Graphs (KGs) contain valuable external knowledge for LLMs. |
| Approach: | They propose to model a conditional subgraph retrieval task handled by small language models and use a subgraph identifier as a special token to retrieve subgraphs. |
| Outcome: | The proposed model achieves competitive retrieval performance compared to state-of-the-art models relying on 7B parameters. |
Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation (2025.acl-long)
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| Challenge: | Multi-hop Question Answering (MHQA) adds layers of complexity to question answering tasks. |
| Approach: | They explore how LMs respond to multi-hop questions by permuting search results under various configurations. |
| Outcome: | The proposed model outperforms decoder-only models in MHQA tasks despite being significantly smaller in size . |
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata (N18-2)
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Lucie-Aimée Kaffee, Hady Elsahar, Pavlos Vougiouklis, Christophe Gravier, Frédérique Laforest, Jonathon Hare, Elena Simperl
| Challenge: | Existing Wikipedia content is unevenly distributed among 287 languages . authors propose a neural network architecture that generates textual summaries from Wikidata triples . |
| Approach: | They propose an automated approach to generate Wikipedia summaries from Wikidata triples using structured data. |
| Outcome: | The proposed approach is tested on Arabic and Esperanto languages with limited editors and content in the most under-resourced Wikipedias. |
Don’t Forget the Base Retriever! A Low-Resource Graph-based Retriever for Multi-hop Question Answering (2025.emnlp-industry)
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Andre Melo, Enting Chen, Pavlos Vougiouklis, Chenxin Diao, Shriram Piramanayagam, Ruofei Lai, Jeff Z. Pan
| Challenge: | Existing GraphRAG approaches to multi-hop question answering rely on expensive LLM calls. |
| Approach: | They propose a lightweight, low-resource, multi-step graph-based retriever for multi-hop QA that performs multi- step retrieval in a few hundred milliseconds. |
| Outcome: | The proposed retriever outperforms conventional retrievers on multi-hop QA datasets and shows strong potential as a base retriever within multi-step agentic frameworks. |
A Usage-centric Take on Intent Understanding in E-Commerce (2024.emnlp-main)
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| Challenge: | Identifying and understanding user intents is a crucial task for E-Commerce. |
| Approach: | They propose to use intent understanding as a natural language reasoning task independent of product ontologies to identify and understand user intents. |
| Outcome: | The proposed framework can't be used to strongly align user intents with products with desirable properties and recommend useful products across diverse categories. |