https://github.com/microsoft/LLaVA-Med
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Papers with Code 아카이브
Instruction Following
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/llmail-inject-challenge
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
https://github.com/microsoft/mttl
https://github.com/mingliiii/layer_gradient
https://github.com/mingliiii/reflection_tuning
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
https://github.com/mingyukim87/synergynerf
DistiLLM: Towards Streamlined Distillation for Large Language Models
https://github.com/ml-gsai/llada
https://github.com/naver-ai/rethinking-proxy-reward
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/neutralzz/billa
https://github.com/nlpxucan/evol-instruct
WizardLM: Empowering Large Language Models to Follow Complex Instructions
https://github.com/nvidia/ruler
https://github.com/open-compass/CIBench
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step
https://github.com/openaccess-ai-collective/axolotl
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
https://github.com/openmedlab/pulse
https://github.com/openpsi-project/realhf
SimPO: Simple Preference Optimization with a Reference-Free Reward
https://github.com/openrobotlab/pointllm
https://github.com/phellonchen/x-llm
https://github.com/phoebussi/alpaca-cot
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/pilancilab/caldera
https://github.com/pwc-1/Paper-9/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/pytorch/rl
https://github.com/qiujihao19/artemis
https://github.com/qizekun/ReCon
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
https://github.com/quangpham2006/SmallPlan
TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning
https://github.com/qwenlm/qwen
https://github.com/qwenlm/qwen-vl
https://github.com/qwenlm/qwen1.5
https://github.com/qwenlm/qwen2
https://github.com/qxb-tyust/location
https://github.com/rabiulcste/vqazero
https://github.com/rainbowluocs/openomni
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis
https://github.com/reliable-information-lab-hevs/benchmark_llm_texts_detection
https://github.com/replicate/cog_stanford_alpaca
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/rlhf-v/rlhf-v
Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants
https://github.com/rui-ye/fedllm-bench
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
https://github.com/runpeidong/act
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.