https://github.com/gyauney/data-similarity-is-not-enough
공식 코드Data Similarity is Not Enough to Explain Language Model Performance
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Data Similarity is Not Enough to Explain Language Model Performance
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
GiT: Towards Generalist Vision Transformer through Universal Language Interface
Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
Benchmark for Uncertainty & Robustness in Self-Supervised Learning
Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On Wikidata using LM probing
Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms
Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms
Decouple knowledge from parameters for plug-and-play language modeling
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.