https://github.com/ArmelRandy/tree-of-problems
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Learning Python Code Suggestion with a Sparse Pointer Network
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Improving Generalization Performance by Switching from Adam to SGD
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
BitNet: Scaling 1-bit Transformers for Large Language Models
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Inductive Learning on Commonsense Knowledge Graph Completion
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.