Topics of interest would include but are not limited to, those below. Please feel free to bring your/your institution’s unique viewpoint and expertise to these topics:

Responsible Generative AI

● This may include, but is not limited to measurement and mitigation, guardrail models, privacy concerns, detecting and mitigating adversarial use cases, and machine unlearning and model disgorgement

● Responsible AI for audio, image and video generation

● Privacy preserving continual learning/self-learning

● Fact Checking and Factual Error Correction for Truthful LLMs

Large Language Models (LLMs)

● Retrieval augmented generation (RAG), fine-tuning and alignment (SFT, RLHF), and efficient inference: ensuring accuracy and reducing hallucinations; maintaining privacy and trust; reasoning over long contexts.

● Long form context methods

● Improving data efficiency; effectively distilling models for real-time inference, data quality checks

● Multi-lingual LLMs and challenges for cross-language defects (e.g. cross-language hallucinations)

● Synthetic data generation for LLM learning

● Adapting LLMs for dynamic content (e.g., feeds, web content) in knowledge-augmented scenarios

● Tool and Code Empowered LLM

● External Knowledge and Domain Knowledge Enhanced LLM and Knowledge Updating

Vision-Language

● Multimodal learning and video understanding: retrieval with multimodal inputs (e.g., video, image, text, speech);

● Adversarial ML with multimodal inputs

● Comprehensive video understanding with diverse content (open-vocabulary).

● Shared multimodal representation spaces, aligned codecs

● LLM and VLM based Intelligent Agents Search and Retrieval:

● Personalization in Search, semantic retrieval, conversational search: understanding descriptive and natural language queries for product search; retrieving information using LLMs' output

● Search page optimization (ranking) using heterogeneous content such as related keywords, shoppable images, videos, and ads

● Tool Learning for Proactive Information Seeking

Efficient Generative AI

● Novel model architectures for improved performance (accuracy & efficiency)

● Training large neural network models with efficiency: High performance distributed training and inference algorithms for Generative AI systems, quality metrics and evaluations

Timeline

Release of CFP: 1 March 2024

One Page Abstracts Due: 01 April 2024

Full Proposals Due: 24 May 2024 at 5pm PT

Announcements of Selected Projects: August 2024

New Projects Start Date: August 2024

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