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Author

Jie Hao

Principal, Applied Scientist

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8 results found

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PB-GRPO: Learning socially adaptive LLM agents from persona-driven simulation with preference-batched GRPO

Jingquan Wang, Jun Yin, Xu Han, Yongsheng Mei, Jie Hao, Bin Guo

COLM 2026, IJCNN 2026

2026

Building LLMs that behave well socially, not merely correctly, requires more than producing locally helpful responses. A socially competent agent must infer users' unstated goals, respect their preferences, and adapt as the conversation unfolds. These behaviors are inherently multi-turn and social, making them hard to optimize: real interaction data is scarce, and user preferences are typically latent rather
Improving contextual query rewrite for conversational AI agents through user-preference feedback learning

Zhongkai Sun, Yingxue Zhou, Jie Hao, Xing Fan, Yanbin Lu, Chengyuan Ma, Wei (Sawyer) Shen, Chenlei (Edward) Guo

EMNLP 2023

2023

Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging the contextual information from previous user-agent conversations to improve the comprehension of current user intent. However, traditional CQR methods often concentrate on supervised fine-tuning only, neglecting the opportunities to learn from user feedback to align with user preferences. Inspired by recent

Conversational AI
Unified contextual query rewriting

Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy, Wei (Sawyer) Shen, Chenlei (Edward) Guo, Gokhan Tur

ACL 2023

2023

Query rewriting (QR) is an important technique for user friction reduction (i.e. recovering ASR error or system error) and contextual carryover (i.e. ellipsis and co-reference) in conversational AI systems. Recently, generation-based QR models have achieved promising results on these two tasks separately. Although these two tasks have many similarities such as they both use the previous dialogue along with

Conversational AI
PENTATRON: PErsonalized coNText-aware transformer for retrieval-based cOnversational uNderstanding

Niranjan Uma Naresh, Ziyan Jiang, Ankit, Sungjin Lee, Jie Hao, Xing Fan, Chenlei (Edward) Guo

EMNLP 2022

2022

Conversational understanding is an integral part of modern intelligent devices. In a large fraction of the global traffic from people using smart digital assistants, frictions in dialogues may be attributed to incorrect understanding of the entities in a user’s query due to factors including ambiguous mentions, mispronunciation, background noise and faulty on-device signal processing. Such errors are compounded

Conversational AI
CGF: Constrained generation framework for query rewriting in conversational AI

Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Chenlei (Edward) Guo, Gokhan Tur

EMNLP 2022

2022

In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands. User frictions are caused by various reasons, such as errors in the conversational AI system, users’ accent or their abridged language. In this work, we present a novel Constrained Generation Framework (CGF) for query rewriting at both global and personalized levels. It is

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