110 citations · 115 across the 17 of their papers we have counts for
20 papers
ToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational Agents
Xiaoyu Wang, Qingqing Gu, Yue Zhao +5
Humans naturally exhibit multiple forms of abstraction in reasoning and interaction, including temporal abstraction across decision timescales and strategic abstraction over commun…
Learn-To-Learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
Luo Ji, Qi Qin, Ningyuan Xi +3
Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes. While finetuning can create the catastrophe forgetting issue, application of meta-learning on L…
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
Teng Chen, Sheng Xu, Feixiang Guo +4
Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial…
Chain-of-Conceptual-Thought Elicits Daily Conversation in Large Language Models
Qingqing Gu, Dan Wang, Yue Zhao +5
Chain-of-Thought (CoT) is widely applied to enhance the LLM capability in math, coding and reasoning tasks. However, its performance is limited for open-domain tasks, when there ar…
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling
Yue Zhao, Xiaoyu Wang, Dan Wang +7
World models have been widely utilized in robotics, gaming, and auto-driving. However, their applications on natural language tasks are relatively limited. In this paper, we constr…
Convert Language Model into a Value-based Strategic Planner
Xiaoyu Wang, Yue Zhao, Qingqing Gu +4
Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. Although large language models (LLMs) have obtained re…
EmoFSM: A Finite State Machine for Emotional Support Conversation
Yue Zhao, Qingqing Gu, Xiaoyu Wang +5
Emotional support conversation (ESC) aims to alleviate people's emotional distress through effective conversations. Although large language models (LLMs) have made remarkable progr…
Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
Luo Ji, Feixiang Guo, Teng Chen +9
Despite the recent advancement in Retrieval-Augmented Generation (RAG) systems, most retrieval methodologies are often developed for factual retrieval, which assumes query and posi…
Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation
Xiaoyu Wang, Ningyuan Xi, Teng Chen +6
Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their app…
MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning
Ningyuan Xi, Xiaoyu Wang, Yetao Wu +7
Current research efforts are focused on enhancing the thinking and reasoning capability of large language model (LLM) by prompting, data-driven emergence and inference-time computa…
Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
Luo Ji, Gao Liu, Mingyang Yin +2
Modern listwise recommendation systems need to consider both long-term user perceptions and short-term interest shifts. Reinforcement learning can be applied on recommendation to s…
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio
Ningyuan Xi, Yetao Wu, Kun Fan +3
Large Language Models (LLM) often need to be Continual Pre-Trained (CPT) to obtain unfamiliar language skills or adapt to new domains. The huge training cost of CPT often asks for…
LaMsS: When Large Language Models Meet Self-Skepticism
Yetao Wu, Yihong Wang, Teng Chen +4
Hallucination is a major challenge for large language models (LLMs), preventing their further application in some fields. The skeptical thinking of humankind could be useful for LL…
M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture
Hongyang Lei, Xiaolong Cheng, Qi Qin +8
Current multimodal learning strategies primarily optimize in the original token space. Such a framework is easy to incorporate with the backbone of pretrained language model, but m…
An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation
Luo Ji, Jiayu Mao, Hailong Shi +3
Online to offline recommendation strongly correlates with the user and service's spatiotemporal information, therefore calling for a higher degree of model personalization. The tra…
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Intra-session Context-aware Feed Recommendation in Live Systems
Luo Ji, Gao Liu, Mingyang Yin +1
Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios. Within a session…
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen +3
Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Jiangchao Yao, Shengyu Zhang, Yang Yao +15
Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the co…
Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation
Luo Ji, Qin Qi, Bingqing Han +1
Recommender system plays a crucial role in modern E-commerce platform. Due to the lack of historical interactions between users and items, cold-start recommendation is a challengin…