Research

We are open to ANY research relevant to NLP & ML. Currently, we are particularly focused on the following topics:

Large Language Models & Foundation Models

• Alignment learning (instruction fine-tuning, RLHF, Imitation learning)

• Complex reasoning (math, code)

• LLM self-correction, self-reflection

• LLM decoding

• AI-Agents

• Plug-in modules (e.g., RAG)

• Multimodality expansion

Machine Learning & Representation Learning

• Uncertatinty measure (measuring confidence of the model output)

• Learning strategies (e.g., self-supervised learning)

• Representation extraction & analysis

• Training model in a restricted environment (Black-box model training & optimization)

• Parameter-efficient training (e.g., pruning, knowledge distillation, quantization)