https://github.com/epflneuroailab/topolm
공식 코드TopoLM: brain-like spatio-functional organization in a topographic language model
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
TopoLM: brain-like spatio-functional organization in a topographic language model
On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists
Faster Causal Attention Over Large Sequences Through Sparse Flash Attention
Interpreting Language Models Through Knowledge Graph Extraction
Will we run out of data? Limits of LLM scaling based on human-generated data
A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP
MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning
DISCO: Distilling Counterfactuals with Large Language Models
Probing Linguistic Information For Logical Inference In Pre-trained Language Models
De-jargonizing Science for Journalists with GPT-4: A Pilot Study
MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
SpeechLMScore: Evaluating speech generation using speech language model
LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech Recognition
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback
ArthModel: Enhance Arithmetic Skills to Large Language Model
LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction
DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMs
AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails
Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors
LAMP: Extracting Text from Gradients with Language Model Priors
Finding Universal Grammatical Relations in Multilingual BERT
Language Models can Self-Improve at State-Value Estimation for Better Search
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.