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Academia Sinica Service LLM Wisdom: Knowing What it Actually Don't Know
Time / Place:
⏱️ 09/15 (Fri.) 14:00-14:30 at R0 - International Conference Hall
Abstract:
With the rise of large language models, many companies and organizations have attempted to leverage this powerful tool to manage their proprietary data. At Academia Sinica, we are also focused on developing a useful LLM service system, SinicaWisdom, to facilitate administrative work. Generally, large language models provide responses based on the information they have encountered in the training data. However, when faced with questions they cannot answer, large language models tend to provide incorrect responses rather than admitting they don't know. This behavior necessitates a significant amount of effort in terms of re-inspection or re-examination.
Furthermore, to restrict the information used by large language models for proprietary data, an information retrieval system is typically employed as the front-end system to initially gather documents relevant to the question. This practice limits the knowledge that LLMs can access and exacerbates this issue. In this presentation, we will discuss how we have enabled SinicaWisdom to "acknowledge what it does not know" and provide additional helpful information instead of simply stating "I cannot answer."
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Biography:
- 古倫維 Lun-Wei Ku
Website: https://homepage.iis.sinica.edu.tw/pages/lwku/contact_en.html - Academia Sinica / Institute of Information Science, Research Fellow
received her Ph.D. degree in Computer Science and Information Engineering from National Taiwan University, Taipei, Taiwan, in 2009. She joined the Institute of Information Science, Academia Sinica as an assistant research fellow in Aug., 2012, and was promoted to be an associate research fellow in Aug., 2018.
Her research expertise lies in natural language processing and information retrieval, especially in sentiment analysis and opinion mining. She often publishes papers in top conferences including ACL, AAAI, SIGIR, WWW, NAACL, and EMNLP. She is very active in the research community, and the international professional activities she involves include the general chair and the program chair of StarSem 2021, StarSem 2019 and AIRS 2019, and the area chair of ACL, NjAACL 2021, ACL, EMNLP, COLING 2020, EMNLP 2019, ACL 2017, CCL 2016, NLPCC 2016, ACL-IJCNLP and EMNLP 2015. Her research is internationally recognized and has been served as the AFNLP Member-at-Large and ACL SIGHAN Asia Information Officer.
She is very experienced in academic and industrial collaborations. Her research collaborators come from US, Singapore, Sweden and Israel, and she is currently working with data companies and banks. Her current research topics focus on recommendation, visual storytelling, sensational text generation, fake news intervention, knowledge-based question answering, lie detection and social media analysis.
- Co-panelists:
黃彥男 Yennun Huang,
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