Xiang Deng

Don’t Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments

2023

A key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to …

Addressing Multi-Label Learning with Partial Labels: From Sample Selection to Label Selection

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

Multi-label Learning with Partial Labels (ML-PL) learns from training data, where each sample is annotated with part of positive labels while leaving the rest of positive labels unannotated. Existing methods mainly focus on extending multi-label …

Iteratively Prompt Pre-trained Language Models for Chain of Thought

2022

While Pre-trained Language Models (PLMs) internalize a great amount of world knowledge, they have been shown incapable of recalling these knowledge to solve tasks requiring complex & multi-step reasoning. Similar to how humans develop a …