7 citations · 14 across the 13 of their papers we have counts for
13 papers
Controlling Multimodal Conversational Agents with Coverage-Enhanced Latent Actions
Yongqi Li, Hao Lang, Tieyun Qian +1
Vision-language models are increasingly employed as multimodal conversational agents (MCAs) for diverse conversational tasks. Recently, reinforcement learning (RL) has been widely…
Understanding Generalization in Role-Playing Models via Information Theory
Yongqi Li, Hao Lang, Fei Huang +2
Role-playing models (RPMs) are widely used in real-world applications but underperform when deployed in the wild. This degradation can be attributed to distribution shifts, includi…
Selective Weak-to-Strong Generalization
Hao Lang, Fei Huang, Yongbin Li
Future superhuman models will surpass the ability of humans and humans will only be able to \textit{weakly} supervise superhuman models. To alleviate the issue of lacking high-qual…
Debate Helps Weak-to-Strong Generalization
Hao Lang, Fei Huang, Yongbin Li
Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the ca…
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Feifan Song, Bowen Yu, Hao Lang +4
Alignment with human preference prevents large language models (LLMs) from generating misleading or toxic content while requiring high-cost human feedback. Assuming resources of hu…
Fine-Tuning Language Models with Reward Learning on Policy
Hao Lang, Fei Huang, Yongbin Li
Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences. RLHF contains three steps, i.e…
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection
Yi Dai, Hao Lang, Kaisheng Zeng +2
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. Recent multi-modal OOD detection leverages textual information from in-distribution…
Long-Tailed Question Answering in an Open World
Yi Dai, Hao Lang, Yinhe Zheng +2
Real-world data often have an open long-tailed distribution, and building a unified QA model supporting various tasks is vital for practical QA applications. However, it is non-tri…
Domain Incremental Lifelong Learning in an Open World
Yi Dai, Hao Lang, Yinhe Zheng +3
Lifelong learning (LL) is an important ability for NLP models to learn new tasks continuously. Architecture-based approaches are reported to be effective implementations for LL mod…
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
Hao Lang, Yinhe Zheng, Binyuan Hui +2
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts. However, most previous OOD intent de…
A Survey on Out-of-Distribution Detection in NLP
Hao Lang, Yinhe Zheng, Yixuan Li +3
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past year…
Estimating Soft Labels for Out-of-Domain Intent Detection
Hao Lang, Yinhe Zheng, Jian Sun +3
Out-of-Domain (OOD) intent detection is important for practical dialog systems. To alleviate the issue of lacking OOD training samples, some works propose synthesizing pseudo OOD s…
Automated Curriculum Learning for Turn-level Spoken Language Understanding with Weak Supervision
Hao Lang, Wen Wang
We propose a learning approach for turn-level spoken language understanding, which facilitates a user to speak one or more utterances compositionally in a turn for completing a tas…