30 citations · 97 across the 15 of their papers we have counts for
16 papers
Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents
Tong Zhao, Reed Li, Yuyang Hu +5
Adapting large language model agents during deployment requires not only retaining past experience, but also turning new observations into timely guidance. Many test-time learning…
OPOD: On-Policy Omni Distillation
Tong Zhao, Yuyang Hu, Yutao Zhu +5
Omni-modal models provide a unified interface for text, images, and audio. However, improving these abilities together remains difficult, as post-training on pooled multimodal data…
AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction
Rui Zou, Yutao Zhu, Mengqi Wei +1
Large language models have demonstrated strong mathematical problem-solving capabilities, yet reliably verifying their candidate answers remains challenging. Existing representativ…
Towards Effective and Efficient Continual Pre-training of Large Language Models
Jie Chen, Zhipeng Chen, Jiapeng Wang +16
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents…
YuLan: An Open-source Large Language Model
Yutao Zhu, Kun Zhou, Kelong Mao +35
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many o…
An Integrated Data Processing Framework for Pretraining Foundation Models
Yiding Sun, Feng Wang, Yutao Zhu +2
The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data. In order to improve data quality, researchers and practitioners ofte…
An Empirical Study of Uniform-Architecture Knowledge Distillation in Document Ranking
Xubo Qin, Xiyuan Liu, Xiongfeng Zheng +2
Although BERT-based ranking models have been commonly used in commercial search engines, they are usually time-consuming for online ranking tasks. Knowledge distillation, which aim…
A Text-guided Protein Design Framework
Shengchao Liu, Yanjing Li, Zhuoxinran Li +10
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format…
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
Yutao Zhu, Jian-Yun Nie, Yixuan Su +3
Contextual information in search sessions is important for capturing users' search intents. Various approaches have been proposed to model user behavior sequences to improve docume…
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Jiawen Wu, Xinyu Zhang, Yutao Zhu +7
Recent years have witnessed great progress on applying pre-trained language models, e.g., BERT, to information retrieval (IR) tasks. Hyperlinks, which are commonly used in Web page…
PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN
Pan Du, Jian-Yun Nie, Yutao Zhu +3
Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability). While a few recent studies have i…
PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling
Yujia Zhou, Zhicheng Dou, Yutao Zhu +1
Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made g…
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
Yutao Zhu, Jian-Yun Nie, Zhicheng Dou +5
Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to…
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
Hongjin Qian, Zhicheng Dou, Yutao Zhu +2
In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of…
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Hao Jiang, Yutao Zhu, Xinyu Zhang +4
Recent years have witnessed great progress on building emotional chatbots. Tremendous methods have been proposed for chatbots to generate responses with given emotions. However, th…
BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
Yutao Zhu, Jian-Yun Nie, Kun Zhou +3
Sentence ordering aims to arrange the sentences of a given text in the correct order. Recent work frames it as a ranking problem and applies deep neural networks to it. In this wor…