Attend NeurIPS 2023 to learn more about the latest papers and attend workshops sponsored by SambaNova and discover how we are continuing to lead the next wave of generative AI.
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 . Furthermore, instruction following powered by LLMs has proven to be effective in multi-modal settings, with applications in image editing  and robotic command execution .
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.
Towards Bridging the Gaps between Machine Learning Research and Regulations
With the increasing deployment of machine learning in diverse applications affecting our daily lives, ethical and legal implications are rising to the forefront. Governments worldwide have responded by implementing regulatory policies to safeguard algorithmic decisions and data usage practices. However, there appears to be a considerable gap between current machine learning research and these regulatory policies. Translating these policies into algorithmic implementations is highly non-trivial, and there may be inherent tensions between different regulatory principles.
This workshop aims to provide a platform for: i) discussing various algorithmic, technical, and policy challenges that arise when operationalizing various guidelines outlined in existing regulatory frameworks, and ii) finding solutions to mitigate and address these challenges.
Foundation Models and Decision Making come together to solve complex tasks at scale
Foundation models pretrained on diverse vision and language datasets have demonstrated exceptional capabilities in performing a wide range of downstream vision and language tasks. As foundation models are deployed in real-world applications such as dialogue, autonomous driving, healthcare, and robotics, they inevitably face new challenges such as learning from external feedback, adapting to different task modalities, and performing long-term reasoning and planning. Such challenges have traditionally been at the core of sequential decision making, encompassing areas such as reinforcement learning, imitation learning, planning, search, and optimal control. These research fields have traditionally focused on task-specific settings with limited prior knowledge, and yet there has been significant research progress in surpassing human performance in tasks like playing board games and Atari video games, as well as operating robots to complete navigation and manipulation tasks. However, since these methods generally learn to solve a specific task from scratch without broad knowledge from vision and language, they can struggle with generalization and sample efficiency.
Research in the intersection of foundation models and sequential decision making is gaining attention. Research in foundation models has expanded to address long-term reasoning and multiple model interactions, while researchers in sequential decision making are developing larger datasets and training larger-scale interactive agents. Further blurring the lines between the two fields, dialogue agents have been optimized by reinforcement learning with human feedback, and large pretrained vision-language models have been used as perception and reasoning components of embodied agents. Foundation models have also been adapted to interact with search engines, calculators, translators, simulators, and program interpreters. Despite these early successes, foundation models for decision making still faces many scientific questions and challenges that have not been addressed by existing work. Examples of questions that we hope to make progress towards answering through this workshop include:
Towards the future of large language models and their emerging descendants
The third version of the Efficient Natural Language and Speech Processing (ENLSP-III) workshop will focus on the future of large language models and their emerging applications on different domains such as natural language, speech processing, and biological sequences; and the target is on how to make them more efficient in terms of Data, Model, Training, and Inference for real-world applications as well as academic research. The workshop program offers an interactive platform for gathering different experts and talents from academia and industry through invited talks, panel discussion, paper submissions, reviews, interactive posters, oral presentations and a mentorship program. This will be a unique opportunity to discuss and share challenging problems, build connections, exchange ideas and brainstorm solutions, and foster future collaborations. The topics of this workshop can be of interest for people working on general machine learning, deep learning, optimization, theory and NLP & Speech applications.