Automation in the medical field, particularly in Intensive Care Units (ICU), has become a primary focus of technological research, with efforts directed towards developing early diagnosis systems, condition prediction...
详细信息
In this study, we investigate the use of mid-sized and open-source large language models to enhance the extraction of geographic information from texts, focusing on toponym resolution. Our approach involves fine-tunin...
详细信息
This research paper investigates efficient fine-tuning techniques for Large Language Models (LLMs) with a focus on minimizing computational costs and memory usage. We explore various Parameter Efficient Fine-Tuning (P...
详细信息
Human evaluation of handwritten answer sheets is very inefficient, inaccurate, and potentially biased. This research work serves the purpose of addressing these drawbacks by developing an automated grading system that...
详细信息
In the oil and gas sector, precise identification and classification of drilling issues are crucial for safety and productivity. Analyzing historical drilling data enables insights into potential problems in similar w...
详细信息
ISBN:
(纸本)9781959025498
In the oil and gas sector, precise identification and classification of drilling issues are crucial for safety and productivity. Analyzing historical drilling data enables insights into potential problems in similar wells drilling. From existing Electronic Drilling Management (EDM) tool, a dataset comprising nearly one hundred thousand text descriptions was compiled through keyword-based text mining alongside anti-keywords. Following the initial labeling process, the data was submitted to the business for label confirmation. Initially, basic machine learning models such as Long short-term memory (LSTM) were used. However, these had limitations related to spelling errors, acronyms, and miscellaneous symbols. Subsequently, the decision was made to transition to Large Language Models (LLMs). To address it, this paper proposes a novel approach using LLMs for multi-label drilling issue classification. Experiments were conducted with various LLMs from different providers and parameter sizes, leveraging GPUs. Challenges arose due to imbalanced data. To enhance the robustness of this method, proper data augmentation was carried out during LLM training to ensure broad coverage of drilling issues. With over 20 distinct classes, drilling descriptions often contain up to 5-6 classes, making achieving singular accuracy challenging. Thus, various accuracy metrics were experimented with to ensure robust multi-label classification (MLC) accuracy that addresses both false positives and false negatives. Regarding overall accuracy, model achieved a level surpassing 90%. Accuracy at the individual class level was evaluated, initially yielding zero accuracy for some classes due to limited occurrences. However, with data augmentation, both recall and precision accuracies improved significantly. Despite the recent surge in the popularity of LLMs, there remains a scarcity of projects effectively utilizing LLMs and Daily Drill Reports (DDR) to correctly identify issues in the well drilling
Reinforcement Learning from Human Feedback (RLHF) has shown great potential in enhancing the alignment of Large Language Models (LLMs) with human preferences. In this study, we introduce a effective approach aimed at ...
详细信息
Given a tree, a set of pebbles initially stationed at some nodes of the tree and a set of target nodes, the Unlabeled Pebble Motion on Trees problem (UPMT) asks to find a plan to move the pebbles one-at-a-time from th...
详细信息
In the field of Information Retrieval and Natural Language Processing, text embeddings play a significant role in tasks such as classification, clustering, and topic modeling. However, extending these embeddings to ab...
详细信息
Document spanners have been proposed as a formal framework for declarative Information Extraction (IE) from text, following IE products from the industry and academia. Over the past decade, the framework has been stud...
详细信息
Identity authentication, a vital part of any application access, is also one way for imposters to gain access to an application using various fingerprint authentication technologies. Therefore, because of the lack of ...
详细信息
暂无评论