LLMs for Complex Insight Generation: Possibilities and Challenges

In this talk, I will share our observations on the potential applications of LLMs in professional financial scenarios. Centered around earnings call data, we explore novel generative tasks, including analyst report generation, generation-time filtering, and Q&A exercises. We will also highlight the challenges and difficulties in evaluation, as well as the importance of Human-AI Interaction in the LLM era. Finally, I will summarize our insights on the LLM era and share potential research directions.

Chung-Chi Chen is currently a researcher at the Artificial Intelligence Research Center, AIST, Japan. His scholarly pursuits revolve around the intricate realm of financial opinion mining and the nuanced understanding and generation of financial documents. He is the founder of ACL SIG-FinTech, and he has orchestrated the FinNLP/FinWeb workshop series within prestigious conferences such as IJCAI, WWW, EMNLP, and IJCNLP-AACL since 2019. He has guided the FinNum and FinArg shared task series on the NTCIR since 2018. He was also a presenter in the AACL-2020, EMNLP-2021, and ECAI-2024 tutorials. He served as Program Co-Chair of NTCIR-18, Senior Area Chair of ACL-2024, and PC member in many representative conferences. In academic competitions, he fortunately won the SIGIR Early Career Researcher Award (Excellence in Community Engagement), in addition to two Thesis Awards and Technology Innovation Award. Beyond academia, he has also ventured into the dynamic realm of FinTech. He earned one prize in a startup competition and four prizes in FinTech competitions. In addition to FinTech, he also has been honored with three prizes in LegalTech competitions.