As artificial intelligence (AI) sweeps across the world, many countries have begun to develop their own large language models, while various industries are evaluating the impact of AI usage on their business. Against this background, Academia Sinica recently released a beta version of its large language model-based chatbot, CKIP-Llama-2-7b, hoping to get feedback from the public and become the starting point for local AI chatbots so that Taiwan will not fall behind other countries.
However, the top-level research institute last week removed the traditional Chinese-language AI chatbot from its Web site after testing by netizens showed some disturbing answers to basic questions such as Taiwan’s national day, anthem and leader. Moreover, netizens found the content provided by Academia Sinica’s chat AI model was not localized enough and made inappropriate verbal choices, as its datasets were provided by several Chinese research institutes, while its dialogue training materials were compiled in simplified Chinese characters.
Academia Sinica admitted the mistake, saying the questionable responses to netizens’ queries were due to the researchers’ use of Chinese datasets in CKIP-Llama-2-7b. As the researchers wanted to save time in developing a chat AI, they simply converted the datasets from simplified Chinese into traditional Chinese characters and put the model online for crowdsourced testing, Academia Sinica explained, adding that it had learned a lesson from the incident and vowed to set up a special task force to avoid repeating the mistakes.
Clearly, it is necessary to establish a Taiwan-based large language model using datasets collected locally, otherwise the content generated by a chat AI would be disputable and controversial on certain issues. Take CKIP-Llama-2-7b as an example: Academia Sinica had claimed its model could be used for academic, commercial, copywriting, literary creation and question-and-answer systems, as well as customer service, language translation, text editing and teaching Chinese. But without datasets taken from local language examples that reflect a Taiwanese context, any homegrown AI would be hard pressed to achieve its expected goals and might be inappropriate to the locality, some AI experts have warned.
This begs the question of whether Taiwan is determined to develop local language datasets and large language models. Granted, doing so is extremely expensive, not only financially, but also in terms of time as well as the massive computing power required, and it poses a challenge to the government to draft the required budget proposal and obtain approval from legislators. It is also very unlikely that private enterprises would invest more resources in software development in addition to hardware upgrades. Nevertheless, it is a fundamental requirement, since large language model AIs need to be trained using massive datasets.
In addition to the localization issue, users of chat AIs, whether it be OpenAI’s ChatGPT or Google’s Bard, face a common problem: inaccuracy and factual errors. Until there is a major breakthrough in AI technology, users’ judgement and knowledge are essential to detect and counter any AI bias, which derives from the algorithms’ tendency to reflect the national, cultural and ideological biases of their creators. Take AI-assisted teaching in Taiwan’s classrooms as an example: Teachers’ ability to judge and correct questionable content generated by AIs is the key to their success in teaching, enabling the technology to greatly boost teaching efficiency.
The CKIP-Llama-2-7b incident serves as a reminder that the use of chat AI on a large scale has national security implications which the government must address urgently. Moreover, this powerful tool has its own fundamental flaws, which require users’ utmost discretion based on their own expertise and judgement, rather than blind trust.
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