Zhen Bi


2024

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OceanGPT: A Large Language Model for Ocean Science Tasks
Zhen Bi | Ningyu Zhang | Yida Xue | Yixin Ou | Daxiong Ji | Guozhou Zheng | Huajun Chen
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet’s surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology.

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EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
Yixin Ou | Ningyu Zhang | Honghao Gui | Ziwen Xu | Shuofei Qiao | Runnan Fang | Lei Li | Zhen Bi | Guozhou Zheng | Huajun Chen
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs). To construct high-quality instruction datasets, many instruction processing approaches have been proposed, aiming to achieve a delicate balance between data quantity and data quality. Nevertheless, due to inconsistencies that persist among various instruction processing methods, there is no standard open-source instruction processing implementation framework available for the community, which hinders practitioners from further developing and advancing. To facilitate instruction processing research and development, we present EasyInstruct, an easy-to-use instruction processing framework for LLMs, which modularizes instruction generation, selection, and prompting, while also considering their combination and interaction. EasyInstruct is publicly released and actively maintained at Github, along with an online demo app and a demo video for quick-start, calling for broader research centered on instruction data and synthetic data.

2022

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CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang | Mosha Chen | Zhen Bi | Xiaozhuan Liang | Lei Li | Xin Shang | Kangping Yin | Chuanqi Tan | Jian Xu | Fei Huang | Luo Si | Yuan Ni | Guotong Xie | Zhifang Sui | Baobao Chang | Hui Zong | Zheng Yuan | Linfeng Li | Jun Yan | Hongying Zan | Kunli Zhang | Buzhou Tang | Qingcai Chen
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.

2020

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OpenUE: An Open Toolkit of Universal Extraction from Text
Ningyu Zhang | Shumin Deng | Zhen Bi | Haiyang Yu | Jiacheng Yang | Mosha Chen | Fei Huang | Wei Zhang | Huajun Chen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Natural language processing covers a wide variety of tasks with token-level or sentence-level understandings. In this paper, we provide a simple insight that most tasks can be represented in a single universal extraction format. We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks. OpenUE allows developers to train custom models to extract information from the text and supports quick model validation for researchers. Besides, OpenUE provides various functional modules to maintain sufficient modularity and extensibility. Except for the toolkit, we also deploy an online demo with restful APIs to support real-time extraction without training and deploying. Additionally, the online system can extract information in various tasks, including relational triple extraction, slot & intent detection, event extraction, and so on. We release the source code, datasets, and pre-trained models to promote future researches in http://github.com/zjunlp/openue.