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cs.CL2025

Personalized LLM Response Generation with Parameterized Memory Injection

Kai Zhang, Yejin Kim, Xiaozhong Liu

Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, personalized LLM response generation holds t…

cs.CL2024

A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis

Kaisong Song, Yangyang Kang, Jiawei Liu +3

User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is…

cs.CL2024

Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs

Chengyuan Liu, Shihang Wang, Lizhi Qing +4

While Large Language Models (LLMs) demonstrate impressive generation abilities, they frequently struggle when it comes to specialized domains due to their limited domain-specific k…

cs.CL2024

More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs

Chengyuan Liu, Yangyang Kang, Shihang Wang +5

The performance on general tasks decreases after Large Language Models (LLMs) are fine-tuned on domain-specific tasks, the phenomenon is known as Catastrophic Forgetting (CF). Howe…

cs.CL2024

RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU

Chengyuan Liu, Shihang Wang, Fubang Zhao +5

Information Extraction (IE) and Text Classification (CLS) serve as the fundamental pillars of NLU, with both disciplines relying on analyzing input sequences to categorize outputs…

cs.CL2024

Enhance Robustness of Language Models Against Variation Attack through Graph Integration

Zi Xiong, Lizhi Qing, Yangyang Kang +5

The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adv…