2 citations · 3 across the 13 of their papers we have counts for
13 papers
Multi-Objective Exploration and Preference Optimization via Mutual Information
Hongyan Xie, Yikun Ban, Ruiyu Fang +4
Aligning large language models with diverse and heterogeneous human values requires multi-objective alignment methods to effectively trade off conflicting preference dimensions. Cu…
Dynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking
Haoxiang Su, Ruiyu Fang, Liting Jiang +2
The performance of task-oriented dialogue models is strongly tied to how well they track dialogue states, which records and updates user information across multi-turn interactions.…
A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction
Jing Bian, Haoxiang Su, Liting Jiang +6
User satisfaction is closely related to enterprises, as it not only directly reflects users' subjective evaluation of service quality or products, but also affects customer loyalty…
UniARM: Towards a Unified Autoregressive Reward Model for Multi-Objective Test-Time Alignment
Hongyan Xie, Yikun Ban, Ruiyu Fang +6
Multi-objective alignment aims to align LLM responses with multiple human preference objectives. Among existing methods, guiding the generation of frozen LLMs through autoregressiv…
Stop Rewarding Hallucinated Steps: Faithfulness-Aware Step-Level Reinforcement Learning for Small Reasoning Models
Shuo Nie, Hexuan Deng, Chao Wang +6
As large language models become smaller and more efficient, small reasoning models (SRMs) are crucial for enabling chain-of-thought (CoT) reasoning in resource-constrained settings…
Training Report of TeleChat3-MoE
Xinzhang Liu, Chao Wang, Zhihao Yang +51
TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one t…
Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges
Di Wu, Ruiyu Fang, Liting Jiang +8
Multi-intent spoken language understanding (SLU) involves two tasks: multiple intent detection and slot filling, which jointly handle utterances containing more than one intent. Ow…
Introducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding
Di Wu, Liting Jiang, Ruiyu Fang +7
Spoken Language Understanding (SLU) consists of two sub-tasks: intent detection (ID) and slot filling (SF). Given its broad range of real-world applications, enhancing SLU for prac…
MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction
Zhongqiu Li, Shiquan Wang, Ruiyu Fang +5
Large language models (LLMs) demonstrate robust capabilities across diverse research domains. However, their performance in universal information extraction (UIE) remains insuffici…
Technical Report of TeleChat2, TeleChat2.5 and T1
Zihan Wang, Xinzhang Liu, Yitong Yao +35
We introduce the latest series of TeleChat models: \textbf{TeleChat2}, \textbf{TeleChat2.5}, and \textbf{T1}, offering a significant upgrade over their predecessor, TeleChat. Despi…
Emotional Support with LLM-based Empathetic Dialogue Generation
Shiquan Wang, Ruiyu Fang, Zhongjiang He +2
Emotional Support Conversation (ESC) aims to provide empathetic and effective emotional assistance through dialogue, addressing the growing demand for mental health support. This p…
RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL
Zhenhe Wu, Zhongqiu Li, Jie Zhang +7
Large language models (LLMs) with in-context learning have significantly improved the performance of text-to-SQL task. Previous works generally focus on using exclusive SQL generat…
TeleChat Technical Report
Zhongjiang He, Zihan Wang, Xinzhang Liu +33
In this technical report, we present TeleChat, a collection of large language models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It includes pretrained language…