Zobrazeno 1 - 10
of 7 547
pro vyhledávání: '"CHEN Qin"'
We provide a detailed overview of various approaches to word segmentation of Asian Languages, specifically Chinese, Korean, and Japanese languages. For each language, approaches to deal with word segmentation differs. We also include our analysis abo
Externí odkaz:
http://arxiv.org/abs/2407.19400
With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success. However, current methods primarily focus on providing solutions or using techniques like Chain-of-Thought to enhance problem-solving accuracy
Externí odkaz:
http://arxiv.org/abs/2407.17349
P-Tailor: Customizing Personality Traits for Language Models via Mixture of Specialized LoRA Experts
Personalized large language models (LLMs) have attracted great attention in many applications, such as intelligent education and emotional support. Most work focuses on controlling the character settings based on the profile (e.g., age, skill, experi
Externí odkaz:
http://arxiv.org/abs/2406.12548
Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works mainly focus on describing the superficial associative relations among
Externí odkaz:
http://arxiv.org/abs/2406.09095
Autor:
Yang, Yutao, Zhou, Jie, Ding, Xuanwen, Huai, Tianyu, Liu, Shunyu, Chen, Qin, He, Liang, Xie, Yuan
Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neural network models, foundation LMs obtain a great ability for transfer
Externí odkaz:
http://arxiv.org/abs/2405.18653
Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis, which aims to extract the aspects and predict their sentiments. Most existing studies focus on improving the performance of the target domain by fine-tuning domain-
Externí odkaz:
http://arxiv.org/abs/2405.05496
Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate responses, by augmenting large language models (LLMs) with the external vast and dynamic knowledge. Most previous work focuses on using RAG for single-round question ans
Externí odkaz:
http://arxiv.org/abs/2403.18243
Document-level Event Causality Identification (DECI) aims to identify causal relations between two events in documents. Recent research tends to use pre-trained language models to generate the event causal relations. Whereas, these methods are prone
Externí odkaz:
http://arxiv.org/abs/2403.11129
Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection. Recent work focuses on combining task-oriented dialogue and chitchat to simulate the interview-based depression diagnosis.
Externí odkaz:
http://arxiv.org/abs/2403.09717
Since large language models (LLMs) achieve significant success in recent years, the hallucination issue remains a challenge, numerous benchmarks are proposed to detect the hallucination. Nevertheless, some of these benchmarks are not naturally genera
Externí odkaz:
http://arxiv.org/abs/2403.00896