Competition Link: https://refind-re.github.io/

@inproceedings{10.1145/3632754.3632756,
author = {Ghosh, Sohom and Umrao, Sachin and Chen, Chung-Chi and Naskar, Sudip Kumar},
title = {The Mask One At a Time Framework for Detecting the Relationship between Financial Entities},
year = {2024},
isbn = {9798400716324},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3632754.3632756},
doi = {10.1145/3632754.3632756},
abstract = {In the financial domain, understanding the relationship between two entities helps in understanding financial texts. In this paper, we introduce the Mask One At a Time (MOAT) framework for detecting the relationship between financial entities. Subsequently, we benchmark its performance with the existing state-of-the-art discriminative and generative Large Language Models (LLMs). We use the SEC-BERT embeddings along with the one-hot encoded vectors of the types of entities and their relation group as features. We benchmark MOAT with four such open-source LLMs, namely, Falcon, Dolly, MPT, and LLaMA-2 under zero-shot and few shot settings. The results prove that MOAT outperforms these LLMs.},
booktitle = {Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation},
pages = {40–43},
numpages = {4},
keywords = {financial texts, large language models, relation extraction},
location = {Panjim, India},
series = {FIRE '23}
}