https://github.com/xijun-cs/vila
공식 코드ViLA: Efficient Video-Language Alignment for Video Question Answering
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
ViLA: Efficient Video-Language Alignment for Video Question Answering
Learning to Maximize Mutual Information for Chain-of-Thought Distillation
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
Cascading Large Language Models for Salient Event Graph Generation
Code4Struct: Code Generation for Few-Shot Event Structure Prediction
Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models
ASR2K: Speech Recognition for Around 2000 Languages without Audio
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
Prioritized Semantic Learning for Zero-shot Instance Navigation
SatLM: Satisfiability-Aided Language Models Using Declarative Prompting
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
Batch Prompting: Efficient Inference with Large Language Model APIs
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning
A Closer Look at the Explainability of Contrastive Language-Image Pre-training
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.