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pro vyhledávání: '"Liu, Yiqun"'
Autor:
Wang, Changyue, Su, Weihang, Yiran, Hu, Ai, Qingyao, Wu, Yueyue, Luo, Cheng, Liu, Yiqun, Zhang, Min, Ma, Shaoping
Recent advances in Large Language Models (LLMs) have significantly shaped the applications of AI in multiple fields, including the studies of legal intelligence. Trained on extensive legal texts, including statutes and legal documents, the legal LLMs
Externí odkaz:
http://arxiv.org/abs/2407.14192
The emergence of Large Language Models (LLMs) has revolutionized how users access information, shifting from traditional search engines to direct question-and-answer interactions with LLMs. However, the widespread adoption of LLMs has revealed a sign
Externí odkaz:
http://arxiv.org/abs/2407.09417
Autor:
Zhan, Jingtao, Ai, Qingyao, Liu, Yiqun, Pan, Yingwei, Yao, Ting, Mao, Jiaxin, Ma, Shaoping, Mei, Tao
For text-to-image generation, automatically refining user-provided natural language prompts into the keyword-enriched prompts favored by systems is essential for the user experience. Such a prompt refinement process is analogous to translating the pr
Externí odkaz:
http://arxiv.org/abs/2407.00247
Autor:
Su, Weihang, Hu, Yiran, Xie, Anzhe, Ai, Qingyao, Que, Zibing, Zheng, Ning, Liu, Yun, Shen, Weixing, Liu, Yiqun
Statute retrieval aims to find relevant statutory articles for specific queries. This process is the basis of a wide range of legal applications such as legal advice, automated judicial decisions, legal document drafting, etc. Existing statute retrie
Externí odkaz:
http://arxiv.org/abs/2406.15313
Identifying and reconstructing what we see from brain activity gives us a special insight into investigating how the biological visual system represents the world. While recent efforts have achieved high-performance image classification and high-qual
Externí odkaz:
http://arxiv.org/abs/2406.07151
Nowadays, personalized recommender systems play an increasingly important role in music scenarios in our daily life with the preference prediction ability. However, existing methods mainly rely on users' implicit feedback (e.g., click, dwell time) wh
Externí odkaz:
http://arxiv.org/abs/2404.15753
In recent years, short video platforms have gained widespread popularity, making the quality of video recommendations crucial for retaining users. Existing recommendation systems primarily rely on behavioral data, which faces limitations when inferri
Externí odkaz:
http://arxiv.org/abs/2404.01008
Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system. As an annually well-known international competition, COLIEE aims to advance the development of state-of-the-art retrieval models for leg
Externí odkaz:
http://arxiv.org/abs/2404.00947
Autor:
Li, Haitao, Ai, Qingyao, Han, Xinyan, Chen, Jia, Dong, Qian, Liu, Yiqun, Chen, Chong, Tian, Qi
Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate r
Externí odkaz:
http://arxiv.org/abs/2403.18435
Autor:
Li, Haitao, Ai, Qingyao, Chen, Jia, Dong, Qian, Wu, Zhijing, Liu, Yiqun, Chen, Chong, Tian, Qi
Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertic
Externí odkaz:
http://arxiv.org/abs/2403.18365