https://github.com/deep-symbolic-mathematics/llm-sr
공식 코드LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation
One2set + Large Language Model: Best Partners for Keyphrase Generation
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents
Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection
Inductive Entity Representations from Text via Link Prediction
EmoNews: A Spoken Dialogue System for Expressive News Conversations
RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts
LOLA -- An Open-Source Massively Multilingual Large Language Model
HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning
Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks
Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning
Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation
Learning to Verify Summary Facts with Fine-Grained LLM Feedback
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.