Week Ending 9.22.2024
RESEARCH WATCH: 9.22.2024
DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data
DRIM addresses a critical challenge in medical data analysis: integrating multimodal and often incomplete data to improve prognosis prediction and treatment discovery. Unlike traditional contrastive learning methods, DRIM captures both shared and unique representations across different modalities like histopathology slides, MRI, and genetic data. This novel approach is particularly valuable in healthcare, where each modality can contain specific, task-relevant information. By effectively handling data sparsity and outperforming state-of-the-art algorithms in glioma patient survival prediction, DRIM shows promise for enhancing medical diagnostics, personalized treatment planning, and advancing our understanding of complex diseases through more comprehensive data integration.
Authors: Lucas Robinet, Ahmad Berjaoui, Ziad Kheil, Elizabeth Cohen-Jonathan Moyal
Link: https://arxiv.org/abs/2409.17055v1
Date: 2024-09-25
Summary:
Real-life medical data is often multimodal and incomplete, fueling the growing need for advanced deep learning models capable of integrating them efficiently. The use of diverse modalities, including histopathology slides, MRI, and genetic data, offers unprecedented opportunities to improve prognosis prediction and to unveil new treatment pathways. Contrastive learning, widely used for deriving representations from paired data in multimodal tasks, assumes that different views contain the same task-relevant information and leverages only shared information. This assumption becomes restrictive when handling medical data since each modality also harbors specific knowledge relevant to downstream tasks. We introduce DRIM, a new multimodal method for capturing these shared and unique representations, despite data sparsity. More specifically, given a set of modalities, we aim to encode a representation for each one that can be divided into two components: one encapsulating patient-related information common across modalities and the other, encapsulating modality-specific details. This is achieved by increasing the shared information among different patient modalities while minimizing the overlap between shared and unique components within each modality. Our method outperforms state-of-the-art algorithms on glioma patients survival prediction tasks, while being robust to missing modalities. To promote reproducibility, the code is made publicly available at https://github.com/Lucas-rbnt/DRIM
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This paper proposes an innovative solution to a significant challenge in Indonesian healthcare: the time-consuming nature of doctor-patient interactions and medical record-keeping in Puskesmas (community health centers). By leveraging large language models for real-time transcription, translation, and summarization of doctor-patient conversations, the system aims to streamline the process of filling out patient forms and creating detailed medical records. This approach has the potential to dramatically improve efficiency in overcrowded healthcare facilities, reduce administrative burdens on doctors, and enhance the quality of patient care. The solution is particularly valuable in linguistically diverse regions, where it can help overcome language barriers and ensure more accurate, comprehensive medical records.
Authors: Azmul Asmar Irfan, Nur Ahmad Khatim, Mansur M. Arief
Link: https://arxiv.org/abs/2409.17054v1
Date: 2024-09-25
Summary:
One of the key issues contributing to inefficiency in Puskesmas is the time-consuming nature of doctor-patient interactions. Doctors need to conduct thorough consultations, which include diagnosing the patient's condition, providing treatment advice, and transcribing detailed notes into medical records. In regions with diverse linguistic backgrounds, doctors often have to ask clarifying questions, further prolonging the process. While diagnosing is essential, transcription and summarization can often be automated using AI to improve time efficiency and help doctors enhance care quality and enable early diagnosis and intervention. This paper proposes a solution using a localized large language model (LLM) to transcribe, translate, and summarize doctor-patient conversations. We utilize the Whisper model for transcription and GPT-3 to summarize them into the ePuskemas medical records format. This system is implemented as an add-on to an existing web browser extension, allowing doctors to fill out patient forms while talking. By leveraging this solution for real-time transcription, translation, and summarization, doctors can improve the turnaround time for patient care while enhancing the quality of records, which become more detailed and insightful for future visits. This innovation addresses challenges like overcrowded facilities and the administrative burden on healthcare providers in Indonesia. We believe this solution will help doctors save time, provide better care, and produce more accurate medical records, representing a significant step toward modernizing healthcare and ensuring patients receive timely, high-quality care, even in resource-constrained settings.
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Models Can and Should Embrace the Communicative Nature of Human-Generated Math
This paper challenges the conventional approach to mathematical modeling in AI by proposing that math should be treated as a form of situated linguistic communication rather than purely symbolic manipulation. The authors argue that language models are well-suited to capture the rich communicative intentions inherent in human-generated math. Through two case studies, they demonstrate how language models interpret mathematical symbols in human-like ways and prefer naturalistically ordered proofs. This perspective opens up new possibilities for developing AI systems that can better understand and generate mathematical content in ways that align with human cognition and communication styles, potentially enhancing math education, scientific communication, and problem-solving applications.
Authors: Sasha Boguraev, Ben Lipkin, Leonie Weissweiler, Kyle Mahowald
Link: https://arxiv.org/abs/2409.17005v1
Date: 2024-09-25
Summary:
Math is constructed by people for people: just as natural language corpora reflect not just propositions but the communicative goals of language users, the math data that models are trained on reflects not just idealized mathematical entities but rich communicative intentions. While there are important advantages to treating math in a purely symbolic manner, we here hypothesize that there are benefits to treating math as situated linguistic communication and that language models are well suited for this goal, in ways that are not fully appreciated. We illustrate these points with two case studies. First, we ran an experiment in which we found that language models interpret the equals sign in a humanlike way -- generating systematically different word problems for the same underlying equation arranged in different ways. Second, we found that language models prefer proofs to be ordered in naturalistic ways, even though other orders would be logically equivalent. We advocate for AI systems that learn from and represent the communicative intentions latent in human-generated math.
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Harnessing Diversity for Important Data Selection in Pretraining Large Language Models
This paper introduces Quad, a novel approach to data selection for pre-training large language models. Recognizing the limitations of current methods that rely solely on data influence scores, Quad incorporates both quality and diversity considerations. By adapting influence computation methods for attention layers and employing a clustering strategy combined with a Multi-Armed Bandit method, Quad achieves a balance between selecting high-quality, influential data instances and ensuring diversity. This approach has the potential to significantly improve the efficiency and effectiveness of pre-training large language models, potentially leading to more robust and generalizable AI systems across various applications.
Authors: Chi Zhang, Huaping Zhong, Kuan Zhang, Chengliang Chai, Rui Wang, Xinlin Zhuang, Tianyi Bai, Jiantao Qiu, Lei Cao, Ye Yuan, Guoren Wang, Conghui He
Link: https://arxiv.org/abs/2409.16986v1
Date: 2024-09-25
Summary:
Data selection is of great significance in pre-training large language models, given the variation in quality within the large-scale available training corpora. To achieve this, researchers are currently investigating the use of data influence to measure the importance of data instances, $i.e.,$ a high influence score indicates that incorporating this instance to the training set is likely to enhance the model performance. Consequently, they select the top-$k$ instances with the highest scores. However, this approach has several limitations. (1) Computing the influence of all available data is time-consuming. (2) The selected data instances are not diverse enough, which may hinder the pre-trained model's ability to generalize effectively to various downstream tasks. In this paper, we introduce \texttt{Quad}, a data selection approach that considers both quality and diversity by using data influence to achieve state-of-the-art pre-training results. In particular, noting that attention layers capture extensive semantic details, we have adapted the accelerated $iHVP$ computation methods for attention layers, enhancing our ability to evaluate the influence of data, $i.e.,$ its quality. For the diversity, \texttt{Quad} clusters the dataset into similar data instances within each cluster and diverse instances across different clusters. For each cluster, if we opt to select data from it, we take some samples to evaluate the influence to prevent processing all instances. To determine which clusters to select, we utilize the classic Multi-Armed Bandit method, treating each cluster as an arm. This approach favors clusters with highly influential instances (ensuring high quality) or clusters that have been selected less frequently (ensuring diversity), thereby well balancing between quality and diversity.
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AXCEL: Automated eXplainable Consistency Evaluation using LLMs
AXCEL addresses a critical challenge in the evaluation of large language models: assessing the consistency of generated text responses. Unlike traditional metrics or recent prompt-based methods, AXCEL offers explainable consistency scores by providing detailed reasoning and identifying inconsistent text spans. Its generalizable nature allows application across multiple tasks without changing the prompt. By outperforming state-of-the-art metrics in detecting inconsistencies across various tasks, AXCEL promises to enhance the reliability and interpretability of AI-generated content. This tool could be particularly valuable in applications requiring high accuracy and transparency, such as automated report generation, content creation, and AI-assisted decision-making systems.
Authors: P Aditya Sreekar, Sahil Verma, Suransh Chopra, Sarik Ghazarian, Abhishek Persad, Narayanan Sadagopan
Link: https://arxiv.org/abs/2409.16984v1
Date: 2024-09-25
Summary:
Large Language Models (LLMs) are widely used in both industry and academia for various tasks, yet evaluating the consistency of generated text responses continues to be a challenge. Traditional metrics like ROUGE and BLEU show a weak correlation with human judgment. More sophisticated metrics using Natural Language Inference (NLI) have shown improved correlations but are complex to implement, require domain-specific training due to poor cross-domain generalization, and lack explainability. More recently, prompt-based metrics using LLMs as evaluators have emerged; while they are easier to implement, they still lack explainability and depend on task-specific prompts, which limits their generalizability. This work introduces Automated eXplainable Consistency Evaluation using LLMs (AXCEL), a prompt-based consistency metric which offers explanations for the consistency scores by providing detailed reasoning and pinpointing inconsistent text spans. AXCEL is also a generalizable metric which can be adopted to multiple tasks without changing the prompt. AXCEL outperforms both non-prompt and prompt-based state-of-the-art (SOTA) metrics in detecting inconsistencies across summarization by 8.7%, free text generation by 6.2%, and data-to-text conversion tasks by 29.4%. We also evaluate the influence of underlying LLMs on prompt based metric performance and recalibrate the SOTA prompt-based metrics with the latest LLMs for fair comparison. Further, we show that AXCEL demonstrates strong performance using open source LLMs.
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This paper provides a comprehensive overview of the rapidly evolving field of large language models (LLMs). Through a systematic literature review, the authors identify key themes in LLM development, impacts, and limitations. The study covers responsible development considerations, algorithmic improvements, ethical challenges, and societal implications of LLMs. By synthesizing current research and identifying future directions, this work serves as a valuable resource for researchers, policymakers, and practitioners in the field of AI. It highlights areas where LLMs could positively impact society while emphasizing the importance of addressing ethical considerations in their development and deployment.
Authors: Zeyneb N. Kaya, Souvick Ghosh
Link: https://arxiv.org/abs/2409.16974v1
Date: 2024-09-25
Summary:
There have been rapid advancements in the capabilities of large language models (LLMs) in recent years, greatly revolutionizing the field of natural language processing (NLP) and artificial intelligence (AI) to understand and interact with human language. Therefore, in this work, we conduct a systematic investigation of the literature to identify the prominent themes and directions of LLM developments, impacts, and limitations. Our findings illustrate the aims, methodologies, limitations, and future directions of LLM research. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications of LLM development. Overall, this paper provides a rigorous and comprehensive overview of current research in LLM and identifies potential directions for future development. The article highlights the application areas that could have a positive impact on society along with the ethical considerations.
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AI-assisted Gaze Detection for Proctoring Online Exams
This study introduces an AI-assisted gaze detection system designed to enhance the security and integrity of high-stakes online exams. By automating the detection of when test-takers look away from the screen, the system helps proctors identify potential rule violations more efficiently. The proposed solution allows proctors to navigate between video frames and discover moments when test-takers may be consulting external resources. This technology has the potential to revolutionize online exam proctoring, making it more effective and less labor-intensive. It could be particularly valuable in the growing field of remote education and certification, ensuring fairness and maintaining the credibility of online assessments.
Authors: Yong-Siang Shih, Zach Zhao, Chenhao Niu, Bruce Iberg, James Sharpnack, Mirza Basim Baig
Link: https://arxiv.org/abs/2409.16923v1
Date: 2024-09-25
Summary:
For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system.
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Cross-lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models
This research compares the performance of self-supervised learning (SSL) models with human capabilities in cross-lingual speech emotion recognition (SER). By conducting a comprehensive analysis of model behavior across different languages and dialects, the study provides insights into the potential of AI systems to adapt to target languages and achieve performance comparable to native speakers. The findings have significant implications for developing more culturally sensitive and linguistically diverse AI applications in fields such as mental health support, customer service, and cross-cultural communication tools. Additionally, the study sheds light on the impact of dialect on human emotion perception, offering valuable insights for both AI development and human-centered design in multilingual contexts.
Authors: Zhichen Han, Tianqi Geng, Hui Feng, Jiahong Yuan, Korin Richmond, Yuanchao Li
Link: https://arxiv.org/abs/2409.16920v1
Date: 2024-09-25
Summary:
Utilizing Self-Supervised Learning (SSL) models for Speech Emotion Recognition (SER) has proven effective, yet limited research has explored cross-lingual scenarios. This study presents a comparative analysis between human performance and SSL models, beginning with a layer-wise analysis and an exploration of parameter-efficient fine-tuning strategies in monolingual, cross-lingual, and transfer learning contexts. We further compare the SER ability of models and humans at both utterance- and segment-levels. Additionally, we investigate the impact of dialect on cross-lingual SER through human evaluation. Our findings reveal that models, with appropriate knowledge transfer, can adapt to the target language and achieve performance comparable to native speakers. We also demonstrate the significant effect of dialect on SER for individuals without prior linguistic and paralinguistic background. Moreover, both humans and models exhibit distinct behaviors across different emotions. These results offer new insights into the cross-lingual SER capabilities of SSL models, underscoring both their similarities to and differences from human emotion perception.
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This paper addresses a critical challenge in the development of Role-Playing Agents (RPAs): their ability to recognize and appropriately respond to queries that conflict with their role-play knowledge. By developing an evaluation benchmark and conducting an in-depth representation-level analysis, the authors propose a lightweight representation editing approach to enhance RPAs' refusal accuracy. This research has significant implications for improving the reliability and safety of AI assistants in various applications, from customer service to educational tools. By enhancing RPAs' ability to refuse inappropriate requests while maintaining their general role-playing capabilities, this work contributes to the development of more trustworthy and context-aware AI systems.
Authors: Wenhao Liu, Siyu An, Junru Lu, Muling Wu, Tianlong Li, Xiaohua Wang, Xiaoqing Zheng, Di Yin, Xing Sun, Xuanjing Huang
Link: https://arxiv.org/abs/2409.16913v1
Date: 2024-09-25
Summary:
Role-Playing Agents (RPAs) have shown remarkable performance in various applications, yet they often struggle to recognize and appropriately respond to hard queries that conflict with their role-play knowledge. To investigate RPAs' performance when faced with different types of conflicting requests, we develop an evaluation benchmark that includes contextual knowledge conflicting requests, parametric knowledge conflicting requests, and non-conflicting requests to assess RPAs' ability to identify conflicts and refuse to answer appropriately without over-refusing. Through extensive evaluation, we find that most RPAs behave significant performance gaps toward different conflict requests. To elucidate the reasons, we conduct an in-depth representation-level analysis of RPAs under various conflict scenarios. Our findings reveal the existence of rejection regions and direct response regions within the model's forwarding representation, and thus influence the RPA's final response behavior. Therefore, we introduce a lightweight representation editing approach that conveniently shifts conflicting requests to the rejection region, thereby enhancing the model's refusal accuracy. The experimental results validate the effectiveness of our editing method, improving RPAs' refusal ability of conflicting requests while maintaining their general role-playing capabilities.
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This position paper explores the integration of Artificial Intelligence (AI) into force-controlled robotic tasks in advanced manufacturing. Focusing on practical applications like deburring, polishing, and assembly tasks, the paper highlights the potential of AI to enhance robotic manipulators' performance in maintaining high-quality production standards. By analyzing recent AI-based methodologies and identifying current challenges, the authors provide valuable insights for future research directions in this field. This work has significant implications for the advancement of Industry 4.0, potentially leading to more efficient, flexible, and intelligent manufacturing processes that can adapt to complex tasks and maintain consistent quality.
Authors: Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio
Link: https://arxiv.org/abs/2409.16828v1
Date: 2024-09-25
Summary:
This position paper explores the integration of Artificial Intelligence (AI) into force-controlled robotic tasks within the scope of advanced manufacturing, a cornerstone of Industry 4.0. AI's role in enhancing robotic manipulators - key drivers in the Fourth Industrial Revolution - is rapidly leading to significant innovations in smart manufacturing. The objective of this article is to frame these innovations in practical force-controlled applications - e.g. deburring, polishing, and assembly tasks like peg-in-hole (PiH) - highlighting their necessity for maintaining high-quality production standards. By reporting on recent AI-based methodologies, this article contrasts them and identifies current challenges to be addressed in future research. The analysis concludes with a perspective on future research directions, emphasizing the need for common performance metrics to validate AI techniques, integration of various enhancements for performance optimization, and the importance of validating them in relevant scenarios. These future directions aim to provide consistency with already adopted approaches, so as to be compatible with manufacturing standards, increasing the relevance of AI-driven methods in both academic and industrial contexts.
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This study proposes a novel deep learning framework for classifying multiple healthcare datasets, focusing on retinal fundus images, cirrhosis stages, and heart disease diagnostics. By combining Residual Networks and Artificial Neural Networks, the authors have developed a hybrid model that outperforms existing approaches in detecting acute and chronic diseases. The framework's high accuracy across diverse medical conditions demonstrates its potential for improving early diagnosis and treatment planning in healthcare. This research could lead to more efficient and accurate analysis of electronic health records, potentially revolutionizing personalized medicine and enhancing patient care across various medical specialties.
Authors: Syed Mohd Faisal Malik, Md Tabrez Nafis, Mohd Abdul Ahad, Safdar Tanweer
Link: https://arxiv.org/abs/2409.16721v1
Date: 2024-09-25
Summary:
In contemporary healthcare, to protect patient data, electronic health records have become invaluable repositories, creating vast opportunities to leverage deep learning techniques for predictive analysis. Retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions have shown promising results through the integration of deep learning techniques for classifying diverse datasets. This study proposes a novel deep learning predictive analysis framework for classifying multiple datasets by pre-processing data from three distinct sources. A hybrid deep learning model combining Residual Networks and Artificial Neural Networks is proposed to detect acute and chronic diseases such as heart diseases, cirrhosis, and retinal conditions, outperforming existing models. Dataset preparation involves aspects such as categorical data transformation, dimensionality reduction, and missing data synthesis. Feature extraction is effectively performed using scaler transformation for categorical datasets and ResNet architecture for image datasets. The resulting features are integrated into a unified classification model. Rigorous experimentation and evaluation resulted in high accuracies of 93%, 99%, and 95% for retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions, respectively. The efficacy of the proposed method is demonstrated through a detailed analysis of F1-score, precision, and recall metrics. This study offers a comprehensive exploration of methodologies and experiments, providing in-depth knowledge of deep learning predictive analysis in electronic health records.
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Task Addition in Multi-Task Learning by Geometrical Alignment
This paper introduces a novel approach to multi-task learning in molecular property prediction, addressing the challenge of limited data availability. By proposing a task addition strategy for the Geometrically Aligned Transfer Encoder (GATE), the authors demonstrate improved performance on target tasks with limited data while minimizing computational complexity. This research has significant implications for drug discovery and materials science, where accurate predictions based on limited data are crucial. The approach could accelerate the development of new pharmaceuticals and materials by enabling more efficient and accurate property predictions across multiple tasks.
Authors: Soorin Yim, Dae-Woong Jeong, Sung Moon Ko, Sumin Lee, Hyunseung Kim, Chanhui Lee, Sehui Han
Link: https://arxiv.org/abs/2409.16645v1
Date: 2024-09-25
Summary:
Training deep learning models on limited data while maintaining generalization is one of the fundamental challenges in molecular property prediction. One effective solution is transferring knowledge extracted from abundant datasets to those with scarce data. Recently, a novel algorithm called Geometrically Aligned Transfer Encoder (GATE) has been introduced, which uses soft parameter sharing by aligning the geometrical shapes of task-specific latent spaces. However, GATE faces limitations in scaling to multiple tasks due to computational costs. In this study, we propose a task addition approach for GATE to improve performance on target tasks with limited data while minimizing computational complexity. It is achieved through supervised multi-task pre-training on a large dataset, followed by the addition and training of task-specific modules for each target task. Our experiments demonstrate the superior performance of the task addition strategy for GATE over conventional multi-task methods, with comparable computational costs.
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Training Language Models to Win Debates with Self-Play Improves Judge Accuracy
This study explores the potential of debate as a method for scalable oversight of language models. By training models to debate through self-play, the authors demonstrate improved accuracy in judge evaluations for long-context reading comprehension tasks. The research shows that debate training encourages stronger and more informative arguments compared to consultancy models. These findings have significant implications for developing more robust and reliable AI systems, particularly in applications requiring complex reasoning and decision-making. The approach could be valuable in fields such as policy analysis, legal reasoning, and scientific discourse, where the ability to present and evaluate competing arguments is crucial.
Authors: Samuel Arnesen, David Rein, Julian Michael
Link: https://arxiv.org/abs/2409.16636v1
Date: 2024-09-25
Summary:
We test the robustness of debate as a method of scalable oversight by training models to debate with data generated via self-play. In a long-context reading comprehension task, we find that language model based evaluators answer questions more accurately when judging models optimized to win debates. By contrast, we find no such relationship for consultancy models trained to persuade a judge without an opposing debater present. In quantitative and qualitative comparisons between our debate models and novel consultancy baselines, we find evidence that debate training encourages stronger and more informative arguments, showing promise that it can help provide high-quality supervision for tasks that are difficult to directly evaluate.
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Entailment-Driven Privacy Policy Classification with LLMs
This paper proposes an innovative framework for classifying paragraphs of privacy policies using Large Language Models (LLMs). By employing an entailment-driven approach, the authors demonstrate significant improvements in classification accuracy compared to traditional LLM methods. This research addresses the critical issue of making privacy policies more accessible and understandable to users, potentially leading to more informed consent in data collection practices. The framework could be applied to develop user-friendly tools for parsing and summarizing privacy policies across various online services, empowering users to make better-informed decisions about their data privacy.
Authors: Bhanuka Silva, Dishanika Denipitiyage, Suranga Seneviratne, Anirban Mahanti, Aruna Seneviratne
Link: https://arxiv.org/abs/2409.16621v1
Date: 2024-09-25
Summary:
While many online services provide privacy policies for end users to read and understand what personal data are being collected, these documents are often lengthy and complicated. As a result, the vast majority of users do not read them at all, leading to data collection under uninformed consent. Several attempts have been made to make privacy policies more user friendly by summarising them, providing automatic annotations or labels for key sections, or by offering chat interfaces to ask specific questions. With recent advances in Large Language Models (LLMs), there is an opportunity to develop more effective tools to parse privacy policies and help users make informed decisions. In this paper, we propose an entailment-driven LLM based framework to classify paragraphs of privacy policies into meaningful labels that are easily understood by users. The results demonstrate that our framework outperforms traditional LLM methods, improving the F1 score in average by 11.2%. Additionally, our framework provides inherently explainable and meaningful predictions.
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Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference
This paper introduces dynamic-width speculative beam decoding (DSBD), a novel approach to improve the efficiency and quality of large language model (LLM) inference. By integrating speculative decoding with beam sampling and introducing adaptive mechanisms, DSBD addresses key challenges in generating high-quality outputs while maintaining efficiency. This research has significant implications for improving the performance of LLMs in real-world applications, potentially leading to faster and more accurate language generation in tasks such as machine translation, content creation, and conversational AI. The proposed method could help make advanced language models more practical and accessible for a wider range of applications and users.
Authors: Zongyue Qin, Zifan He, Neha Prakriya, Jason Cong, Yizhou Sun
Link: https://arxiv.org/abs/2409.16560v1
Date: 2024-09-25
Summary:
Large language models (LLMs) have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slow and costly. Speculative decoding has emerged as a promising solution, leveraging a smaller auxiliary model to draft future tokens, which are then validated simultaneously by the larger model, achieving a speed-up of 1-2x. Although speculative decoding matches the same distribution as multinomial sampling, multinomial sampling itself is prone to suboptimal outputs, whereas beam sampling is widely recognized for producing higher-quality results by maintaining multiple candidate sequences at each step. This paper explores the novel integration of speculative decoding with beam sampling. However, there are four key challenges: (1) how to generate multiple sequences from the larger model's distribution given drafts sequences from the small model; (2) how to dynamically optimize the number of beams to balance efficiency and accuracy; (3) how to efficiently verify the multiple drafts in parallel; and (4) how to address the extra memory costs inherent in beam sampling. To address these challenges, we propose dynamic-width speculative beam decoding (DSBD). Specifically, we first introduce a novel draft and verification scheme that generates multiple sequences following the large model's distribution based on beam sampling trajectories from the small model. Then, we introduce an adaptive mechanism to dynamically tune the number of beams based on the context, optimizing efficiency and effectiveness. Besides, we extend tree-based parallel verification to handle multiple trees simultaneously, accelerating the verification process. Finally, we illustrate a simple modification to our algorithm to mitigate the memory overhead of beam sampling...
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SynChart: Synthesizing Charts from Language Models
This paper explores the potential of using large language models (LLMs) alone for generating synthetic data to train competitive multi-modality models for chart understanding. By creating SynChart, a large-scale dataset of diverse chart images with dense annotations, the authors demonstrate the ability to train a chart-expert model that achieves near-GPT-4O performance on the ChartQA task. This research has significant implications for developing more efficient and cost-effective methods for training multi-modality AI models, particularly in domains where obtaining large-scale, annotated datasets is challenging. The approach could accelerate advancements in visual understanding and question-answering systems across various fields, from business intelligence to scientific data analysis.
Authors: Mengchen Liu, Qixiu Li, Dongdong Chen, Dong Chen, Jianmin Bao, Yunsheng Li
Link: https://arxiv.org/abs/2409.16517v1
Date: 2024-09-25
Summary:
With the release of GPT-4V(O), its use in generating pseudo labels for multi-modality tasks has gained significant popularity. However, it is still a secret how to build such advanced models from its base large language models (LLMs). This work explores the potential of using LLMs alone for data generation and develop competitive multi-modality models focusing on chart understanding. We construct a large-scale chart dataset, SynChart, which contains approximately 4 million diverse chart images with over 75 million dense annotations, including data tables, code, descriptions, and question-answer sets. We trained a 4.2B chart-expert model using this dataset and achieve near-GPT-4O performance on the ChartQA task, surpassing GPT-4V.
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This paper introduces GSplatLoc, a novel approach to visual localization that leverages 3D Gaussian Splatting (3DGS) and dense keypoint descriptors. By distilling keypoint descriptors into 3DGS, the authors demonstrate improved spatial understanding and more accurate camera pose predictions. This research addresses key challenges in visual localization, such as high memory consumption and extensive optimization requirements. The proposed method has significant implications for applications in augmented reality, robotics, and autonomous navigation, where accurate and efficient visual localization is crucial. GSplatLoc's superior performance over state-of-the-art methods suggests its potential to enhance the reliability and precision of visual localization systems in various real-world scenarios.
Authors: Gennady Sidorov, Malik Mohrat, Ksenia Lebedeva, Ruslan Rakhimov, Sergey Kolyubin
Link: https://arxiv.org/abs/2409.16502v1
Date: 2024-09-24
Summary:
Although various visual localization approaches exist, such as scene coordinate and pose regression, these methods often struggle with high memory consumption or extensive optimization requirements. To address these challenges, we utilize recent advancements in novel view synthesis, particularly 3D Gaussian Splatting (3DGS), to enhance localization. 3DGS allows for the compact encoding of both 3D geometry and scene appearance with its spatial features. Our method leverages the dense description maps produced by XFeat's lightweight keypoint detection and description model. We propose distilling these dense keypoint descriptors into 3DGS to improve the model's spatial understanding, leading to more accurate camera pose predictions through 2D-3D correspondences. After estimating an initial pose, we refine it using a photometric warping loss. Benchmarking on popular indoor and outdoor datasets shows that our approach surpasses state-of-the-art Neural Render Pose (NRP) methods, including NeRFMatch and PNeRFLoc.
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This paper evaluates the use of transformer-based models for real-time detection and localization of electronic components in Waste Printed Circuit Boards (WPCBs). By achieving superior performance compared to state-of-the-art object detection models like YOLOv8 and YOLOv9, the proposed approach demonstrates its potential for improving the recycling of Critical Raw Materials (CRMs) from electronic waste. This research has significant implications for enhancing the efficiency and economic viability of electronic waste recycling processes. The developed system could lead to more effective recovery of valuable materials from discarded electronics, contributing to sustainable resource management and reducing environmental impact in the electronics industry.
Authors: Muhammad Mohsin, Stefano Rovetta, Francesco Masulli, Alberto Cabri
Link: https://arxiv.org/abs/2409.16496v1
Date: 2024-09-24
Summary:
Critical Raw Materials (CRMs) such as copper, manganese, gallium, and various rare earths have great importance for the electronic industry. To increase the concentration of individual CRMs and thus make their extraction from Waste Printed Circuit Boards (WPCBs) convenient, we have proposed a practical approach that involves selective disassembling of the different types of electronic components from WPCBs using mechatronic systems guided by artificial vision techniques. In this paper we evaluate the real-time accuracy of electronic component detection and localization of the Real-Time DEtection TRansformer model architecture. Transformers have recently become very popular for the extraordinary results obtained in natural language processing and machine translation. Also in this case, the transformer model achieves very good performances, often superior to those of the latest state of the art object detection and localization models YOLOv8 and YOLOv9.
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This comprehensive study of Parameter-Efficient Transfer Learning (PETL) methods in Vision Transformers provides valuable insights into their performance and applicability. By systematically comparing various PETL methods, the authors uncover new findings about their accuracy, complementarity, and robustness to distribution shifts. This research offers practical guidance for choosing and implementing PETL methods in different scenarios, from low-shot to many-shot regimes. The study's findings have significant implications for improving the efficiency and effectiveness of transfer learning in visual recognition tasks, potentially leading to more resource-efficient and adaptable AI systems across various applications in computer vision and beyond.
Authors: Zheda Mai, Ping Zhang, Cheng-Hao Tu, Hong-You Chen, Li Zhang, Wei-Lun Chao
Link: https://arxiv.org/abs/2409.16434v1
Date: 2024-09-24
Summary:
Parameter-efficient transfer learning (PETL) has attracted significant attention lately, due to the increasing size of pre-trained models and the need to fine-tune (FT) them for superior downstream performance. This community-wide enthusiasm has sparked a plethora of new methods. Nevertheless, a systematic study to understand their performance and suitable application scenarios is lacking, leaving questions like when to apply PETL and which method to use largely unanswered. In this paper, we conduct a unifying empirical study of representative PETL methods in the context of Vision Transformers. We systematically tune their hyper-parameters to fairly compare their accuracy on downstream tasks. Our study not only offers a valuable user guide but also unveils several new insights. First, if tuned carefully, different PETL methods can obtain quite similar accuracy in the low-shot benchmark VTAB-1K. This includes simple methods like FT the bias terms that were reported inferior. Second, though with similar accuracy, we find that PETL methods make different mistakes and high-confidence predictions, likely due to their different inductive biases. Such an inconsistency (or complementariness) opens up the opportunity for ensemble methods, and we make preliminary attempts at this. Third, going beyond the commonly used low-shot tasks, we find that PETL is also useful in many-shot regimes -- it achieves comparable and sometimes better accuracy than full FT, using much fewer learnable parameters. Last but not least, we investigate PETL's ability to preserve a pre-trained model's robustness to distribution shifts (e.g., a CLIP backbone). Perhaps not surprisingly, PETL methods outperform full FT alone. However, with weight-space ensembles, the fully FT model can achieve a better balance between downstream and out-of-distribution performance, suggesting a future research direction for PETL.
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A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions
This survey paper provides a comprehensive overview of biases in Large Language Models (LLMs), addressing a critical concern in the widespread deployment of these powerful AI systems. By systematically categorizing biases, synthesizing current research findings, and discussing their implications in real-world applications, the authors offer a valuable resource for researchers, practitioners, and policymakers. The paper's critical assessment of existing bias mitigation techniques and proposed future research directions contributes to the ongoing efforts to enhance fairness and equity in AI systems. This work has significant implications for developing more responsible and unbiased AI technologies across various domains, from natural language processing to decision-making systems.
Authors: Rajesh Ranjan, Shailja Gupta, Surya Narayan Singh
Link: https://arxiv.org/abs/2409.16430v1
Date: 2024-09-24
Summary:
Large Language Models(LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. However, their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases. We systematically categorize biases into several dimensions. Our survey synthesizes current research findings and discusses the implications of biases in real-world applications. Additionally, we critically assess existing bias mitigation techniques and propose future research directions to enhance fairness and equity in LLMs. This survey serves as a foundational resource for researchers, practitioners, and policymakers concerned with addressing and understanding biases in LLMs.
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