Chunking
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Benchmarks
CoNLL 2000
Penn Treebank
CoNLL 2003 (English)
CoNLL 2003 (German)
CoNLL 2003
Most implemented
Bidirectional LSTM-CRF Models for Sequence Tagging
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
NCRF++: An Open-source Neural Sequence Labeling Toolkit
Semi-supervised Multitask Learning for Sequence Labeling
Papers
Dynamic Chunking for End-to-End Hierarchical Sequence Modeling
Major progress on language models (LMs) in recent years has largely resulted from moving away from specialized models designed for specific tasks, to general models based on powerful architectures (e.g. the Transformer) …
ChunkingCLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs
Large language models (LLMs), including zero-shot and few-shot paradigms, have shown promising capabilities in clinical text generation. However, real-world applications face two key challenges: (1) patient data is highl…
ChunkingRAGRetrievalRetrieval-augmented Generation+1Can LLMs Replace Humans During Code Chunking?
Large language models (LLMs) have become essential tools in computer science, especially for tasks involving code understanding and generation. However, existing work does not address many of the unique challenges presen…
ChunkingCronusVLA: Transferring Latent Motion Across Time for Multi-Frame Prediction in Manipulation
Recent vision-language-action (VLA) models built on pretrained vision-language models (VLMs) have demonstrated strong generalization across manipulation tasks. However, they remain constrained by a single-frame observati…
ChunkingVision-Language-ActioncAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
Retrieval-Augmented Generation (RAG) has become essential for large-scale code generation, grounding predictions in external code corpora to improve actuality. However, a critical yet underexplored aspect of RAG pipeline…
ChunkingCode GenerationRAGRetrieval+1Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems are increasingly vital for navigating the ever-expanding body of scientific literature, particularly in high-stakes domains such as chemistry. Despite the promise of RAG, foun…
ChunkingRAGRetrievalRetrieval-augmented Generation