Instruction Following
516 papers with code • 1 benchmarks • 14 datasets
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.
Benchmarks
These leaderboards are used to track progress in Instruction Following
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Datasets
IFEval
GSCAN
MIMIC-IT
UGIF
Bactrian-X
CIDAR
Most implemented papers
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Applying our method to the vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations.
QLoRA: Efficient Finetuning of Quantized LLMs
artidoro/qlora
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NeurIPS 2023
Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99. 3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
Habitat: A Platform for Embodied AI Research
facebookresearch/habitat-sim
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ICCV 2019
We present Habitat, a platform for research in embodied artificial intelligence (AI).
Visual Instruction Tuning
haotian-liu/LLaVA
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NeurIPS 2023
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model.
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
thudm/chatglm-6b
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We introduce ChatGLM, an evolving family of large language models that we have been developing over time.
Qwen2.5 Technical Report
qwenlm/qwen2.5
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In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
We propose to decompose instruction execution to goal prediction and action generation.
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
ziyuguo99/point-bind_point-llm
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We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video.
