Miguel de Carvalho — accepted AI-conference papers

2025

FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval

Ying Li, Mengyu Wang, Miguel de Carvalho, Sotirios Sabanis, Tiejun Ma

EMNLP 2025

Financial disclosures such as 10-K filings pose challenging retrieval problems because of their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We present Financial Mapping-Guided Enhanced Answer Retrieval, a re

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning

Mengyu Wang, Sotirios Sabanis, Miguel de Carvalho, Shay B. Cohen, Tiejun Ma

EMNLP 2025

Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (QA). In this work, we propose Expert Question Decomposition (EQD), an approach designed to balance the use of domain know

Cited by 0 Source ↗PDF ↗Scholar ↗