CS Events

Qualifying Exam Multi-LLM Collaboration for Automatic Data and Representation Engineering
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Speaker: Sam Lin

Bio

Location : Remote Via Zoom

Committee: 

Professor Yongfeng Zhang

Professor Ruixiang Tang

Professor Dong Deng

Professor Mario Szegedy

Event Type: Qualifying Exam

Abstract:  Large Language Models (LLMs) are increasingly deployed as general-purpose inference engines. Prior research has shown that LLM behavior can be highly sensitive to how task inputs are presented. Motivated by this, our work studies a paradigm that collaboratively leverages multiple LLMs as automatic, task-specific data and representation engineers to transform raw task inputs (or outputs) to satisfy downstream objectives and constraints, either as a pre-processing step prior to inference or as training signals for fine-tuning. Specifically, we study this paradigm in three settings: (i) privacy-preserving text obfuscation that reduces sensitive exposure while largely maintaining task utility; (ii) data optimization via automated content engineering and structural reformulation to systematically improve downstream performance beyond prompt-only tuning; and (iii) generative retrieval with learned indexing, where we learn hierarchical semantic indices for items and perform retrieval via constrained decoding to guarantee validity, enabling an end-to-end, cold-start-friendly framework that avoids multi-stage retrieval pipelines.Publications Included:EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs (https://aclanthology.org/2025.naacl-long.614/)ADO: Automatic Data Optimization for Inputs in LLM Prompts (https://aclanthology.org/2025.findings-acl.1340/)Multi-layer Generative Item Retrieval for Natural Language Queries (preprint attached)

Organization: 

Contact  Professor Yongfeng Zhang

Zoom Link: https://rutgers.zoom.us/j/91441118655?pwd=CVyQI3dPXQXoTJrrp7hAw7gqbPahje.1