Thanks Huck Yang and Pin-Yu Chen for hosting the amazing tutorial! It's my pleasure to present with you 🎉🎉🎉

Thank you for the feedback that enhances the survey's comprehensiveness.

Tomorrow, I'll be presenting part of the tutorial at ICASSP 2024. The topics include speech LM and ways to prompt it and parameter-efficient learning to enable text LLM to understand speech. See you there!

🚨New paper!🚨
🦙LLMs make mistakes in long reasoning steps

🤔️Why don’t we instead ask LLMs to generate Python programs to perform complex reasoning for us?

💪We propose NLEP to solve both symbolic/natural language tasks, beats GPT4 with CoT, without task-specific example!👇

I will be at #ACL2023NLP next week ✈️ to share our three papers on diverse topics. Looking forward to meeting old friends and making some new friends. ✨ Stop by our poster if you want to chat! 😁

Attending #ICASSP2023 in Rhodes, Greece? Don't miss the workshop on 'Self-supervision in Audio, Speech & Beyond'. Dive deep into the advancements in self-supervised learning. Catch me delivering the workshop keynote @ Jupiter Ballroom, 8:40 a.m. GMT+3.

sites.google.com/view/icassp-sa…

Happy to share that our 'Parameter-Efficient Learning' tutorial serves as the most popular session in day 1 IEEE ICASSP with over 120 in-person attendees interested in advances in adapters, reprograms, and prompts! Pin-Yu Chen Hung-yi Lee (李宏毅) NTU SPML Lab #AmazonScience Amazon Science