2024-2025 USC-Amazon Center Call for Proposals
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
