Text understanding and language tasks
Hi guys, I'm a PhD student in Applied AI and I've been building an embeddable graph database engine from scratch in Rust. I'd love feedba...
I keep seeing people recommend chatgpt for financial modeling and I need to push back because I spent a month testing it for multifamily ...
The war’s economic effects are hitting all sorts of fossil-derived products.
![[2602.22224] DS SERVE: A Framework for Efficient and Scalable Neural Retrieval](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
DS SERVE is a framework designed to enhance neural retrieval systems by efficiently processing large-scale text datasets, achieving low l...
This article presents a comparative analysis of neural retriever-reranker pipelines for retrieval-augmented generation (RAG) in e-commerc...
This article presents a novel framework for recommending quotations that are both unexpected and rational, enhancing the writing experien...
The paper presents RAGdb, a novel architecture for Retrieval-Augmented Generation (RAG) that simplifies multimodal data processing by eli...
This article discusses a Retrieval-Augmented Generation (RAG) assistant designed for Anatomical Pathology laboratories, enhancing access ...
The paper presents Taxoria, a novel pipeline that enhances existing taxonomies using Large Language Models (LLMs), addressing issues of l...
This survey reviews Neural Routing Solvers (NRSs) that utilize deep learning for vehicle routing problems, highlighting their heuristic n...
The paper presents EMPO², a novel hybrid RL framework that enhances exploration in memory-augmented LLM agents, achieving significant per...
The paper introduces NoRA, a novel approach to Low-Rank Adaptation (LoRA) that overcomes the limitations of linear methods by utilizing m...
SC-Arena introduces a natural language benchmark for evaluating single-cell reasoning in large language models, addressing gaps in curren...
This article presents a theoretical analysis of multi-agent imitation learning (MAIL) in linear Markov games, introducing a novel interac...
FactGuard introduces an innovative framework for detecting video misinformation using reinforcement learning, enhancing the capabilities ...
This paper introduces LITE, a new strategy for accelerating the pre-training of large language models (LLMs) by optimizing training dynam...
This article presents a machine learning framework for forecasting antimicrobial resistance (AMR) trends using WHO GLASS data, highlighti...
This paper introduces a novel approach called CEEH, which combines difficulty-aware entropy regularization with reinforcement learning to...
The paper presents ContextRL, a framework that enhances knowledge discovery efficiency in multi-layered language models (MLLMs) through c...
FlexMS is a new framework designed for benchmarking deep learning models used in mass spectrum prediction within metabolomics, addressing...
This paper presents Layer-wise MIA-risk-aware DP-SGD, a method to reduce Membership Inference Attack risks in machine learning models by ...
The paper presents pQuant, a novel approach for low-bit language models that utilizes decoupled linear quantization-aware training to enh...
The paper presents Taesar, a data-centric framework designed to enhance recommendation model performance by addressing data sparsity and ...
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