@inproceedings{guo-etal-2024-dictllm,
title = "{D}ict{LLM}: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics",
author = "Guo, YiQiu and
Yang, Yuchen and
Zhang, Ya and
Wang, Yu and
Wang, Yanfeng",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.609",
pages = "10231--10241",
abstract = "Structured data offers an efficient means of organizing information. Exsisting text-serialization based methods for processing structured data using large language models (LLMs) are not designed to explicitly capture the heterogeneity of structured data. Such methods are suboptimal for LLMs to process structured data, and may lead to large input token size and poor robustness to input perturbation. In this paper, we propose a novel framework called DictLLM, which is an efficient and effective framework for the modeling of medical lab report to deal with the report-assisted diagnosis generation task. DictLLM introduce 1) group positional encoding to maintain the permutation invariance, 2) hierarchical attention bias to capture the inductive bias of structured data, and 3) a optimal transport alignment layer to align the embeddings generated by the dict encoder with the LLM, producing a list of fixed-length virtual tokens. We conduct experiments with multiple LLM models on a large-scale real-world medical lab report dataset for automatic diagnosis generation. The results show that our proposed framework outperforms the baseline methods and few-shot GPT-4 in terms of both Rouge-L and Knowledge F1 score. We also conduct multiple experiments and analyze the scalability and robustness of our proposed framework, demonstrating the superiority of our method in modeling the heterogeneous structure of medical dictionaries data.",
}
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<abstract>Structured data offers an efficient means of organizing information. Exsisting text-serialization based methods for processing structured data using large language models (LLMs) are not designed to explicitly capture the heterogeneity of structured data. Such methods are suboptimal for LLMs to process structured data, and may lead to large input token size and poor robustness to input perturbation. In this paper, we propose a novel framework called DictLLM, which is an efficient and effective framework for the modeling of medical lab report to deal with the report-assisted diagnosis generation task. DictLLM introduce 1) group positional encoding to maintain the permutation invariance, 2) hierarchical attention bias to capture the inductive bias of structured data, and 3) a optimal transport alignment layer to align the embeddings generated by the dict encoder with the LLM, producing a list of fixed-length virtual tokens. We conduct experiments with multiple LLM models on a large-scale real-world medical lab report dataset for automatic diagnosis generation. The results show that our proposed framework outperforms the baseline methods and few-shot GPT-4 in terms of both Rouge-L and Knowledge F1 score. We also conduct multiple experiments and analyze the scalability and robustness of our proposed framework, demonstrating the superiority of our method in modeling the heterogeneous structure of medical dictionaries data.</abstract>
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%0 Conference Proceedings
%T DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics
%A Guo, YiQiu
%A Yang, Yuchen
%A Zhang, Ya
%A Wang, Yu
%A Wang, Yanfeng
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F guo-etal-2024-dictllm
%X Structured data offers an efficient means of organizing information. Exsisting text-serialization based methods for processing structured data using large language models (LLMs) are not designed to explicitly capture the heterogeneity of structured data. Such methods are suboptimal for LLMs to process structured data, and may lead to large input token size and poor robustness to input perturbation. In this paper, we propose a novel framework called DictLLM, which is an efficient and effective framework for the modeling of medical lab report to deal with the report-assisted diagnosis generation task. DictLLM introduce 1) group positional encoding to maintain the permutation invariance, 2) hierarchical attention bias to capture the inductive bias of structured data, and 3) a optimal transport alignment layer to align the embeddings generated by the dict encoder with the LLM, producing a list of fixed-length virtual tokens. We conduct experiments with multiple LLM models on a large-scale real-world medical lab report dataset for automatic diagnosis generation. The results show that our proposed framework outperforms the baseline methods and few-shot GPT-4 in terms of both Rouge-L and Knowledge F1 score. We also conduct multiple experiments and analyze the scalability and robustness of our proposed framework, demonstrating the superiority of our method in modeling the heterogeneous structure of medical dictionaries data.
%U https://aclanthology.org/2024.findings-acl.609
%P 10231-10241
Markdown (Informal)
[DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics](https://aclanthology.org/2024.findings-acl.609) (Guo et al., Findings 2024)
ACL