https://github.com/snumprlab/capeam
공식 코드Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Instruction following is the basic task of the model. This task is dedicated to evaluating the ability of the large model to follow human instructions. It is hoped that the model can generate controllable and safe answers.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents
Online Continual Learning For Interactive Instruction Following Agents
ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environments
A Dual-Space Framework for General Knowledge Distillation of Large Language Models
ProgCo: Program Helps Self-Correction of Large Language Models
FILM: Following Instructions in Language with Modular Methods
Don't Copy the Teacher: Data and Model Challenges in Embodied Dialogue
LLark: A Multimodal Instruction-Following Language Model for Music
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP
"No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy
Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
EventHallusion: Diagnosing Event Hallucinations in Video LLMs
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology
LLaVA-VSD: Large Language-and-Vision Assistant for Visual Spatial Description
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
Self-Instruct: Aligning Language Models with Self-Generated Instructions
LeVo: High-Quality Song Generation with Multi-Preference Alignment
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.