Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing最新文献_第2页

README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP. 通过以数据为中心的NLP，为患者教育架起医学术语和外行理解的桥梁。

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2024-11-01 DOI: 10.18653/v1/2024.findings-emnlp.737

Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, Hong Yu

{"title":"README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.","authors":"Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, Hong Yu","doi":"10.18653/v1/2024.findings-emnlp.737","DOIUrl":"10.18653/v1/2024.findings-emnlp.737","url":null,"abstract":"The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR). However, medical jargon in EHRs poses significant challenges in patient comprehension. To address this, we introduce a new task of automatically generating lay definitions, aiming to simplify complex medical terms into patient-friendly lay language. We first created the README dataset, an extensive collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions, each offering context-aware lay definitions manually annotated by domain experts. We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality. We then used README as the training data for models and leveraged a Retrieval-Augmented Generation method to reduce hallucinations and improve the quality of model outputs. Our extensive automatic and human evaluations demonstrate that open-source mobile-friendly models, when fine-tuned with high-quality data, are capable of matching or even surpassing the performance of state-of-the-art closed-source large language models like ChatGPT. This research represents a significant stride in closing the knowledge gap in patient education and advancing patient-centric healthcare solutions .","PeriodicalId":521132,"journal":{"name":"Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing","volume":"2024 ","pages":"12609-12629"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12912280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146222513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large Language Models are In-context Teachers for Knowledge Reasoning. 大型语言模型是知识推理的语境教师。

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2024-11-01 DOI: 10.18653/v1/2024.findings-emnlp.961

Jiachen Zhao, Zonghai Yao, Zhichao Yang, Hong Yu

{"title":"Large Language Models are In-context Teachers for Knowledge Reasoning.","authors":"Jiachen Zhao, Zonghai Yao, Zhichao Yang, Hong Yu","doi":"10.18653/v1/2024.findings-emnlp.961","DOIUrl":"10.18653/v1/2024.findings-emnlp.961","url":null,"abstract":"In this work, we study in-context teaching (ICT), where a teacher provides in-context example rationales to teach a student to reason over unseen cases. Human teachers are usually required to craft in-context demonstrations, which are costly and have high variance. We ask whether a large language model (LLM) can serve as a more effective in-context teacher for itself or other LLMs, compared to humans. Inspired by the Encoding Specificity Hypothesis from human episodic memory, we hypothesize that in-context exemplars crafted by the teacher should match the training data of the student. This hypothesis motivates us to propose Self-Explain where an LLM's self-elicited explanations are used as in-context demonstrations for prompting it as they are generalized from the model's training examples. Self-Explain is shown to significantly outperform using human-crafted exemplars and other baselines. Furthermore, we reveal that for ICT, rationales from different teacher LLMs or human experts that more resemble the student LLM's self-explanations are better in-context demonstrations. This supports our encoding specificity hypothesis. We then propose Teach-Back that aligns a teacher LLM with the student to enhance the ICT performance. For example, Teach-Back enables a 7B model to teach the much larger GPT-3.5 in context, surpassing human teachers by around 5% in test accuracy on medical question answering.","PeriodicalId":521132,"journal":{"name":"Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing","volume":"2024 ","pages":"16470-16486"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12833938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146069808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models. 预训练医学视觉语言模型中防御对抗噪声的轻量级微调方法。

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2024-11-01 DOI: 10.18653/v1/2024.findings-emnlp.633

Xu Han, Linghao Jin, Xuezhe Ma, Xiaofeng Liu

{"title":"Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models.","authors":"Xu Han, Linghao Jin, Xuezhe Ma, Xiaofeng Liu","doi":"10.18653/v1/2024.findings-emnlp.633","DOIUrl":"10.18653/v1/2024.findings-emnlp.633","url":null,"abstract":"Fine-tuning pre-trained Vision-Language Models (VLMs) has shown remarkable capabilities in medical image and textual depiction synergy. Nevertheless, many pre-training datasets are restricted by patient privacy concerns, potentially containing noise that can adversely affect downstream performance. Moreover, the growing reliance on multi-modal generation exacerbates this issue because of its susceptibility to adversarial attacks. To investigate how VLMs trained on adversarial noisy data perform on downstream medical tasks, we first craft noisy upstream datasets using multi-modal adversarial attacks. Through our comprehensive analysis, we unveil that moderate noise enhances model robustness and transferability, but increasing noise levels negatively impact downstream task performance. To mitigate this issue, we propose rectify adversarial noise (RAN) framework, a recipe designed to effectively defend adversarial attacks and rectify the influence of upstream noise during fine-tuning.","PeriodicalId":521132,"journal":{"name":"Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing","volume":"2024 ","pages":"10784-10799"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12665184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145656786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models. 大型预训练语言模型参数有效微调的预算引导迭代搜索。

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2024-11-01 DOI: 10.18653/v1/2024.findings-emnlp.437

Aofei Chang, Jiaqi Wang, Han Liu, Parminder Bhatia, Cao Xiao, Ting Wang, Fenglong Ma

{"title":"BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models.","authors":"Aofei Chang, Jiaqi Wang, Han Liu, Parminder Bhatia, Cao Xiao, Ting Wang, Fenglong Ma","doi":"10.18653/v1/2024.findings-emnlp.437","DOIUrl":"10.18653/v1/2024.findings-emnlp.437","url":null,"abstract":"Parameter Efficient Fine-Tuning (PEFT) offers an efficient solution for fine-tuning large pretrained language models for downstream tasks. However, most PEFT strategies are manually designed, often resulting in suboptimal performance. Recent automatic PEFT approaches aim to address this issue but face challenges such as search space entanglement, inefficiency, and lack of integration between parameter budgets and search processes. To overcome these issues, we introduce a novel Budget-guided Iterative search strategy for automatic PEFT (BIPEFT), which significantly enhances search efficiency. BIPEFT employs a new iterative search strategy to disentangle the binary module and rank dimension search spaces. Additionally, we design early selection strategies based on parameter budgets, accelerating the learning process by gradually removing unimportant modules and fixing rank dimensions. Extensive experiments on public benchmarks demonstrate the superior performance of BIPEFT in achieving efficient and effective PEFT for downstream tasks with a low parameter budget.","PeriodicalId":521132,"journal":{"name":"Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing","volume":"2024 ","pages":"7429-7440"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12667107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145663663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variational Language Concepts for Interpreting Foundation Language Models. 解释基础语言模型的变分语言概念。

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2024-11-01 DOI: 10.18653/v1/2024.findings-emnlp.505

Hengyi Wang, Shiwei Tan, Zhqing Hong, Desheng Zhang, Hao Wang

引用次数: 0

When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? 当原始数据盛行：大型语言模型嵌入在医疗机器学习应用的数值数据表示中有效吗？

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2024-11-01 DOI: 10.18653/v1/2024.findings-emnlp.311

Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy A Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar

{"title":"When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?","authors":"Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy A Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar","doi":"10.18653/v1/2024.findings-emnlp.311","DOIUrl":"10.18653/v1/2024.findings-emnlp.311","url":null,"abstract":"The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivotal in clinical contexts, into LLM paradigms has not been thoroughly explored. In this study, we examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record (EHR) data. We compare the performance of these embeddings with that of raw numerical EHR data when used as feature inputs to traditional machine learning (ML) algorithms that excel at tabular data learning, such as eXtreme Gradient Boosting. We focus on instruction-tuned LLMs in a zero-shot setting to represent abnormal physiological data and evaluating their utilities as feature extractors to enhance ML classifiers for predicting diagnoses, length of stay, and mortality. Furthermore, we examine prompt engineering techniques on zero-shot and few-shot LLM embeddings to measure their impact comprehensively. Although findings suggest the raw data features still prevail in medical ML tasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a promising avenue for future research in medical applications.","PeriodicalId":521132,"journal":{"name":"Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing","volume":"2024 ","pages":"5414-5428"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models. 冷：零射击自定义可解释的特征提取与大型语言模型的临床笔记。

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-12-01 DOI: 10.18653/v1/2023.findings-emnlp.568

Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C Wallace

{"title":"CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models.","authors":"Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C Wallace","doi":"10.18653/v1/2023.findings-emnlp.568","DOIUrl":"10.18653/v1/2023.findings-emnlp.568","url":null,"abstract":"We propose CHiLL (Crafting High-Level Latents), an approach for natural-language specification of features for linear models. CHiLL prompts LLMs with expert-crafted queries to generate interpretable features from health records. The resulting noisy labels are then used to train a simple linear classifier. Generating features based on queries to an LLM can empower physicians to use their domain expertise to craft features that are clinically meaningful for a downstream task of interest, without having to manually extract these from raw EHR. We are motivated by a real-world risk prediction task, but as a reproducible proxy, we use MIMIC-III and MIMIC-CXR data and standard predictive tasks (e.g., 30-day readmission) to evaluate this approach. We find that linear models using automatically extracted features are comparably performant to models using reference features, and provide greater interpretability than linear models using \"Bag-of-Words\" features. We verify that learned feature weights align well with clinical expectations.","PeriodicalId":521132,"journal":{"name":"Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing","volume":"2023 ","pages":"8477-8494"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12747426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145867363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings. 无监督句子嵌入的层次对比学习。

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing Pub Date : 2023-12-01 DOI: 10.18653/v1/2023.findings-emnlp.161

Zhuofeng Wu, Chaowei Xiao, Vg Vinod Vydiswaran

{"title":"HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings.","authors":"Zhuofeng Wu, Chaowei Xiao, Vg Vinod Vydiswaran","doi":"10.18653/v1/2023.findings-emnlp.161","DOIUrl":"10.18653/v1/2023.findings-emnlp.161","url":null,"abstract":"In this paper, we propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training efficiency and effectiveness. Traditional methods typically encode a sequence in its entirety for contrast with others, often neglecting local representation learning, leading to challenges in generalizing to shorter texts. Conversely, HiCL improves its effectiveness by dividing the sequence into several segments and employing both local and global contrastive learning to model segment-level and sequence-level relationships. Further, considering the quadratic time complexity of transformers over input tokens, HiCL boosts training efficiency by first encoding short segments and then aggregating them to obtain the sequence representation. Extensive experiments show that HiCL enhances the prior top-performing SNCSE model across seven extensively evaluated STS tasks, with an average increase of +0.2% observed on BERTlarge and +0.44% on RoBERTalarge.","PeriodicalId":521132,"journal":{"name":"Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing","volume":"2023 ","pages":"2461-2476"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12716160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0