Instruction Following
262 papers with code • 1 benchmarks • 14 datasets
Libraries
Use these libraries to find Instruction Following models and implementationsDatasets
GSCAN
IFEval
MIMIC-IT
UGIF
Bactrian-X
CIDAR
Most implemented papers
AlpaGasus: Training A Better Alpaca with Fewer Data
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.
L-Eval: Instituting Standardized Evaluation for Long Context Language Models
openlmlab/leval
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Recently, there has been growing interest in extending the context length of large language models (LLMs), aiming to effectively process long inputs of one turn or conversations with more extensive histories.
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
waltonfuture/InstructionGPT-4
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To achieve this, we first propose several metrics to access the quality of multimodal instruction data.
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
We propose a memory-efficient finetuning algorithm for large language models (LLMs) that supports finetuning LLMs with 65B parameters in 2/3/4-bit precision on as little as one 24GB GPU.
Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic
We demonstrate the effectiveness of RESTA in both parameter-efficient and full fine-tuning, covering a wide range of downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math.
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
qizekun/ShapeLLM
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This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
The Replica Dataset: A Digital Replica of Indoor Spaces
We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale.
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
We find that, using our approach, agents can learn to solve to diverse, temporally-extended tasks such as object sorting and multi-object rearrangement, including from raw pixel observations.
Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions
jhu-lcsr/good_robot
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Conference On Robot Learning (CoRL) 2021
Our model completes block manipulation tasks with synthetic commands 530 more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning.
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
xfgao/dialfred
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Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively.
