https://github.com/jwlin/arxiv-160430
공식 코드Using Semantic Similarity for Input Topic Identification in Crawling-based Web Application Testing
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Using Semantic Similarity for Input Topic Identification in Crawling-based Web Application Testing
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
MindGPT: Interpreting What You See with Non-invasive Brain Recordings
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
Concept-Level Explainability for Auditing & Steering LLM Responses
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense
WatChat: Explaining perplexing programs by debugging mental models
PsyEval: A Suite of Mental Health Related Tasks for Evaluating Large Language Models
MISR: Measuring Instrumental Self-Reasoning in Frontier Models
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
Gradient Ascent Post-training Enhances Language Model Generalization
Aligning to Thousands of Preferences via System Message Generalization
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.