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Owl: A large language model for it operations
With the rapid development of IT operations, it has become increasingly crucial to efficiently
manage and analyze large volumes of data for practical applications. The techniques of …
manage and analyze large volumes of data for practical applications. The techniques of …
Logformer: A pre-train and tuning pipeline for log anomaly detection
Log anomaly detection is a key component in the field of artificial intelligence for IT
operations (AIOps). Considering log data of variant domains, retraining the whole network …
operations (AIOps). Considering log data of variant domains, retraining the whole network …
Stochastic rag: End-to-end retrieval-augmented generation through expected utility maximization
H Zamani, M Bendersky - Proceedings of the 47th International ACM …, 2024 - dl.acm.org
This paper introduces Stochastic RAG--a novel approach for end-to-end optimization of
retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of …
retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of …
Conceptmath: A bilingual concept-wise benchmark for measuring mathematical reasoning of large language models
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained
benchmark that evaluates concept-wise mathematical reasoning of Large Language Models …
benchmark that evaluates concept-wise mathematical reasoning of Large Language Models …
Emerge: Integrating rag for improved multimodal ehr predictive modeling
The integration of multimodal Electronic Health Records (EHR) data has notably advanced
clinical predictive capabilities. However, current models that utilize clinical notes and …
clinical predictive capabilities. However, current models that utilize clinical notes and …
An information bottleneck perspective for effective noise filtering on retrieval-augmented generation
Retrieval-augmented generation integrates the capabilities of large language models with
relevant information retrieved from an extensive corpus, yet encounters challenges when …
relevant information retrieved from an extensive corpus, yet encounters challenges when …
M2C: towards automatic multimodal manga complement
Multimodal manga analysis focuses on enhancing manga understanding with visual and
textual features, which has attracted considerable attention from both natural language …
textual features, which has attracted considerable attention from both natural language …
EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation
The integration of multimodal Electronic Health Records (EHR) data has significantly
advanced clinical predictive capabilities. Existing models, which utilize clinical notes and …
advanced clinical predictive capabilities. Existing models, which utilize clinical notes and …
MLAD: A Unified Model for Multi-system Log Anomaly Detection
In spite of the rapid advancements in unsupervised log anomaly detection techniques, the
current mainstream models still necessitate specific training for individual system datasets …
current mainstream models still necessitate specific training for individual system datasets …
Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation
Incorporating external knowledge into dialogue generation (KIDG) is crucial for improving
the correctness of response, where evidence fragments serve as knowledgeable snippets …
the correctness of response, where evidence fragments serve as knowledgeable snippets …