https://github.com/launchnlp/lito
공식 코드Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression
Transformer Meets Twicing: Harnessing Unattended Residual Information
RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction
NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus
A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models
Large Language Models as Realistic Microservice Trace Generators
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model
Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding
Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation
LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification Task
Clover: Towards A Unified Video-Language Alignment and Fusion Model
FocusLLM: Precise Understanding of Long Context by Dynamic Condensing
Language Representations Can be What Recommenders Need: Findings and Potentials
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
DE-COP: Detecting Copyrighted Content in Language Models Training Data
InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.