https://github.com/microsoft/LLMLingua
공식 코드LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Inference with Reference: Lossless Acceleration of Large Language Models
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention
MPNet: Masked and Permuted Pre-training for Language Understanding
A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
TAPEX: Table Pre-training via Learning a Neural SQL Executor
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations
SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention
RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning
Substance over Style: Document-Level Targeted Content Transfer
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Efficient Long Sequence Modeling via State Space Augmented Transformer
GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
Glinthawk: A Two-Tiered Architecture for Offline LLM Inference
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.