https://github.com/suu990901/LLaMA-InfoEntropy-Loss
공식 코드MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models
BoK: Introducing Bag-of-Keywords Loss for Interpretable Dialogue Response Generation
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
Anti-LM Decoding for Zero-shot In-context Machine Translation
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Training Software Engineering Agents and Verifiers with SWE-Gym
In-context Pretraining: Language Modeling Beyond Document Boundaries
LLaVA-VSD: Large Language-and-Vision Assistant for Visual Spatial Description
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
Emergence of Social Norms in Generative Agent Societies: Principles and Architecture
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models
Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
Large language model for Bible sentiment analysis: Sermon on the Mount
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
Symphony Generation with Permutation Invariant Language Model
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding
Language-Enhanced Representation Learning for Single-Cell Transcriptomics
Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.