Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
- URL: http://arxiv.org/abs/2410.15702v1
- Date: Mon, 21 Oct 2024 07:19:19 GMT
- Title: Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
- Authors: Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen,
- Abstract summary: We introduce ALternate Contrastive Decoding (ALCD) to solve hallucination issues in medical information extraction tasks.
ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.
- Score: 92.32881381717594
- License:
- Abstract: The impressive capabilities of large language models (LLMs) have attracted extensive interests of applying LLMs to medical field. However, the complex nature of clinical environments presents significant hallucination challenges for LLMs, hindering their widespread adoption. In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD). We begin by redefining MIE tasks as an identify-and-classify process. We then separate the identification and classification functions of LLMs by selectively masking the optimization of tokens during fine-tuning. During the inference stage, we alternately contrast output distributions derived from sub-task models. This approach aims to selectively enhance the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs. Additionally, we propose an alternate adaptive constraint strategy to more effectively adjust the scale and scope of contrastive tokens. Through comprehensive experiments on two different backbones and six diverse medical information extraction tasks, ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.
Related papers
- Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration Decoding [66.06337890279839]
Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding for downstream multi-modal tasks.
LVLMs still suffer from generating hallucinations in complex generation tasks, leading to inconsistencies between visual inputs and generated content.
We propose an Inter-Modality Correlation Decoding (IMCCD) method to mitigate hallucinations in LVLMs in a training-free manner.
arXiv Detail & Related papers (2025-01-03T17:56:28Z) - RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks.
Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs.
In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
arXiv Detail & Related papers (2024-10-18T03:45:19Z) - Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning [16.883679810267342]
Iterative Model-level Contrastive Learning (Iter-AHMCL) to address hallucination.
This paper introduces a novel approach called Iterative Model-level Contrastive Learning (Iter-AHMCL) to address hallucination.
arXiv Detail & Related papers (2024-10-16T00:15:40Z) - LLM4Brain: Training a Large Language Model for Brain Video Understanding [9.294352205183726]
We introduce an LLM-based approach for reconstructing visual-semantic information from fMRI signals elicited by video stimuli.
We employ fine-tuning techniques on an fMRI encoder equipped with adaptors to transform brain responses into latent representations aligned with the video stimuli.
In particular, we integrate self-supervised domain adaptation methods to enhance the alignment between visual-semantic information and brain responses.
arXiv Detail & Related papers (2024-09-26T15:57:08Z) - CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models [51.70129969269271]
We introduce a novel contrastive-based decoding method, COuntering DEscription Contrastive Decoding (CODE)
Our method significantly reduces hallucinations and improves cross-modal consistency across various benchmarks and cutting-edge LMMs.
arXiv Detail & Related papers (2024-06-04T03:04:21Z) - OPTiML: Dense Semantic Invariance Using Optimal Transport for Self-Supervised Medical Image Representation [6.4136876268620115]
Self-supervised learning (SSL) has emerged as a promising technique for medical image analysis due to its ability to learn without annotations.
We introduce a novel SSL framework OPTiML, employing optimal transport (OT), to capture the dense semantic invariance and fine-grained details.
Our empirical results reveal OPTiML's superiority over state-of-the-art methods across all evaluated tasks.
arXiv Detail & Related papers (2024-04-18T02:59:48Z) - Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding [25.489832294197797]
This paper introduces the Instruction Contrastive Decoding (ICD) method, a novel approach designed to reduce hallucinations during LVLM inference.
Our method is inspired by our observation that what we call disturbance instructions significantly exacerbate hallucinations in multimodal fusion modules.
arXiv Detail & Related papers (2024-03-27T16:04:47Z) - Debiasing Multimodal Large Language Models [61.6896704217147]
Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing.
Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image.
To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
arXiv Detail & Related papers (2024-03-08T12:35:07Z) - Large Language Model Distilling Medication Recommendation Model [58.94186280631342]
We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)
Our research aims to transform existing medication recommendation methodologies using LLMs.
To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
arXiv Detail & Related papers (2024-02-05T08:25:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.