Transformers and Large Language Models: Revolutionizing Natural Language Processing and Beyond
Transformer-based Large Language Models (LLMs) have significantly expanded the scope of natural language processing (NLP) applications, transcending their initial use in chatbot technology. These models demonstrate versatility in diverse domains, from code interpretation and image captioning to facilitating interactive systems and advancing computational fields.
HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization
HumanEval-XL introduces a comprehensive benchmark for multilingual code generation, addressing the gap in evaluating cross-lingual NL generalization of LLMs.
PE: A Poincare Explanation Method for Fast Text Hierarchy Generation
PE introduces a novel method using hyperbolic spaces to model feature interactions efficiently, demonstrating effectiveness in generating hierarchical explanations.
Improving Sequence-to-Sequence Models for Abstractive Text Summarization Using Meta Heuristic Approaches
Enhancing sequence-to-sequence models for abstractive text summarization through meta heuristic approaches.
LlamBERT: Large-scale Low-Cost Data Annotation in NLP
Efficiently leverage LLMs for cost-effective data annotation in NLP.
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
mPLUG-Owl introduces a novel training paradigm to enhance large language models with multimodal abilities through modularized learning.
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation
EthioLLM introduces multilingual language models for five Ethiopian languages, addressing the lack of resources in low-resource languages.
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Large Language Models face knowledge cutoff issues, but EasyEdit offers an efficient solution.
AraPoemBERT: Pretrained Arabic Poetry Analysis Model
Arabic poetry analysis benefits from AraPoemBERT, outperforming other models in various NLP tasks.
BED: Bi-Encoder-Based Detectors for Out-of-Distribution Detection
提案されたバイエンコーダーベースの検出器は、NLPにおけるOOD検出において他の手法を凌駕し、ラベル付きOODサンプルを必要としない場合でも優れた性能を発揮します。
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