Author: Isaac Young

Authors: Nicholas Pipitone、Ghita Houir Alami Paper: https://arxiv.org/abs/2408.10343 Introduction In the rapidly evolving landscape of AI in the legal sector, Retrieval-Augmented Generation (RAG) systems have emerged as a crucial technology. These systems combine retrieval mechanisms with generative large language models (LLMs) to provide contextualized generation. However, a critical gap in the ecosystem remains unaddressed: the lack of a dedicated benchmark for evaluating the retrieval component in legal-specific RAG systems. Existing benchmarks, such as LegalBench, assess the reasoning capabilities of LLMs on complex legal questions but do not evaluate the retrieval quality over a large corpus, which is crucial for RAG-based systems.…

Authors: Zhirong Huang、Shichao Zhang、Debo Cheng、Jiuyong Li、Lin Liu、Guangquan Lu Paper: https://arxiv.org/abs/2408.09651 Introduction Background With the rapid expansion of the internet, the volume of available information has grown exponentially, making it increasingly challenging for users to find content that aligns with their preferences. Recommender systems have emerged as a crucial solution to this problem by analyzing user behavior data to deliver personalized recommendations, thereby enhancing user engagement and satisfaction. These systems are integral to many digital platforms, including e-commerce, streaming media, and social networks, significantly improving information retrieval efficiency and user experience. Problem Statement Despite their success, recommender systems often suffer from…

Authors: Aman Ahluwalia、Bishwajit Sutradhar、Karishma Ghosh Paper: https://arxiv.org/abs/2408.09236 Introduction In the realm of information retrieval, traditional keyword-based search engines have long been the standard. These engines identify documents containing the queried terms but often fall short in capturing the user’s true intent, leading to irrelevant results. This paper introduces a novel hybrid search approach that leverages the strengths of non-semantic search engines, Large Language Models (LLMs), and embedding models to address these limitations. The proposed system integrates keyword matching, semantic vector embeddings, and LLM-generated structured queries to deliver highly relevant and contextually appropriate search results. By combining these complementary methods, the…

Authors: Usman Syed、Ethan Light、Xingang Guo、Huan Zhang、Lianhui Qin、Yanfeng Ouyang、Bin Hu Paper: https://arxiv.org/abs/2408.08302 Introduction The rapid advancements in artificial intelligence (AI) have significantly transformed various domains, including transportation system engineering. Among these advancements, large language models (LLMs) such as GPT-4, Claude 3.5 Sonnet, and Llama 3.1 have shown remarkable capabilities in understanding and generating human-like text. This paper explores the potential of these LLMs in solving undergraduate-level transportation engineering problems, focusing on their accuracy, consistency, and reasoning behaviors. Transportation systems engineering is a critical interdisciplinary subfield of civil engineering that involves the planning, design, operations, and management of transportation systems. The complexity…

Authors: Lachlan McGinness、Peter Baumgartner Paper: https://arxiv.org/abs/2408.07854 Machine learning (ML) has revolutionized the way we solve problems and automate tasks. However, the interpretability of ML models remains a significant challenge. In this blog, we delve into a novel approach called CON-FOLD, an extension of the FOLD-RM algorithm, which introduces confidence scores to enhance the explainability of ML models. Introduction Machine learning models, while powerful, often lack transparency. Decision trees are an exception, providing a clear set of rules for decision-making. The FOLD (First Order Learner of Default) algorithm, introduced by Shakerin et al., generates interpretable rules but can sometimes be misleading.…

Authors: Jiaojiao Guan、Yongxin Ji、Cheng Peng、Wei Zou、Xubo Tang、Jiayu Shang、Yanni Sun Paper: https://arxiv.org/abs/2408.06402 PhaGO: Protein Function Annotation for Bacteriophages by Integrating Genomic Context Introduction Bacteriophages, or phages, are viruses that infect bacterial cells and are abundant in various environments such as animal gastrointestinal tracts, water bodies, and soil. They play a crucial role in microbial ecology by influencing bacterial adaptation, evolution, and population dynamics. Due to the increasing threat of antibiotic resistance, phages are gaining attention as potential alternatives to traditional antibiotics. Understanding phage biology, including virus infection, replication, and evolution, requires accurate annotation of phage proteins. However, the diversity and scarcity…

Authors: Laurens Walleghem Paper: https://arxiv.org/abs/2408.06797 Stunned by Sleeping Beauty: How Prince Probability Updates His Forecast Upon Their Fateful Encounter Introduction The Sleeping Beauty problem is a well-known puzzle in probability theory that has sparked extensive debate since its introduction by Elga in 2000. The problem involves Sleeping Beauty being put to sleep, followed by a coin toss. If the coin lands on Tails, she is woken up on Monday, put back to sleep with her memory erased, and then woken up again on Tuesday. If the coin lands on Heads, she is woken up only on Monday. Each time she…