Instruction Following
286 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
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.
Precise Zero-Shot Dense Retrieval without Relevance Labels
Given a query, HyDE first zero-shot instructs an instruction-following language model (e. g. InstructGPT) to generate a hypothetical document.
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
Large Language Models (LLMs) are increasingly being integrated into various applications.
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
In this paper, we present our finding that prepending a Task-Agnostic Prefix Prompt (TAPP) to the input improves the instruction-following ability of various Large Language Models (LLMs) during inference.
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github. com/camel-ai/camel.
Instruction Tuning with GPT-4
Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed.
Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models.
X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages
(3) Integrating multiple modalities: all single-modal encoders are aligned with the LLM through X2L interfaces to integrate multimodal capabilities into the LLM.
