Petros Raptopoulos

dblp:409/8253 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Question answering and dialogue systems · 77% Multi-agent systems · 23%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Question answering and dialogue systems › domain-specific question answering
legal question answering
0.912025
PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements · EMNLP 2025
Information retrieval
retrieval-augmented generation
0.912025
PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements · EMNLP 2025

Methods — techniques the papers use, named apart from their topics

retrieval-augmented generation · 1.7multi-agent framework · 1.7
YearPublicationVenuePosition
2025 PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
abstract
Contract review is a complex and time-intensive task that typically demands specialized legal expertise, rendering it largely inaccessible to non-experts. Moreover, legal interpretation is rarely straightforward—ambiguity is pervasive, and judgments often hinge on subjective assessments. Compounding these challenges, contracts are usually confidential, restricting their use with proprietary models and necessitating reliance on open-source alternatives. To address these challenges, we introduce PAKTON: a fully open-source, end-to-end, multi-agent framework with plug-and-play capabilities. PAKTON is designed to handle the complexities of contract analysis through collaborative agent workflows and a novel retrieval-augmented generation (RAG) component, enabling automated legal document review that is more accessible, adaptable, and privacy-preserving. Experiments demonstrate that PAKTON outperforms both general-purpose and pretrained models in predictive accuracy, retrieval performance, explainability, completeness, and grounded justifications as evaluated through a human study and validated with automated metrics.
Petros Raptopoulos, Giorgos Filandrianos, Maria Lymperaiou, Giorgos B. Stamou
EMNLP1