Recent advancements in training large language models (LLMs) to follow “instructions” have significantly increased their ability to comprehend open-ended language commands, encompassing a wide range of needs, preferences, and values.

This remarkable transformation has led to the creation of remarkable industrial models such as GPT-4 and Bard, as well as an increased focus within the open-source and research communities: creating new benchmark and resources [1,2], developing new training methods [3,4], and understanding the limitations of these methods [5]. Furthermore, instruction following powered by LLMs has proven to be effective in multi-modal settings, with applications in image editing [6] and robotic command execution [7].

We organize this workshop to facilitate discussions on advancing instruction tuning methodologies and constructing general-purpose instruction-following models. We believe it is crucial to organize this workshop due to the prevalence of proprietary models with restricted access, thereby creating the need for an open platform to encourage discussions. Moreover, we aim to foster interdisciplinary collaboration by bringing together researchers from diverse fields such as natural language processing, computer vision, robotics, human-computer interaction, AI safety, among others, to share their latest findings and explore potential avenues for future research.

Centering on “instructions,” we invite submissions covering various topics, including but not limited to the list below:

• Modeling: algorithms and pipelines for learning from instructions and human feedback; designing training objectives and rewards; training and inference efficiency
• Data Collection: crowd-sourcing; synthetic data generation; data democratization
• Evaluation and Oversight: effective and reliable oversight over existing models; enforcing guardrails and guarantees for model behaviors; interpretability and analysis
• Engineering and Open-sourcing: best practice in training, evaluation and deployment; open-sourcing efforts; openness and reproducibility
• Applications: long-context, multi-round and personalized instruction-following models
• Multimodal and Multidisciplinary: instruction following models for computer vision, robotics, games, art, etc.
• Limitations, Risks and Safety: bias and fairness; factuality and hallucination; safety concerns arising from instruction-following models
• Other adjacent research topics (e.g., in-context learning, prompting, multi-task learning) that enable better responses to instructions in dynamic environments

Speakers

Panelists

Stay tuned! We plan to have two panels to encourage discussion!

Organizers

Steering Committee

Sponsors

We need your help! Email us (an-instructive-workshop@googlegroups.com) if you are interested in sponsoring our workshop!