Swaroop is a research scientist at Google-Deepmind, working on improving Gemini. His research expertise includes instruction tuning and different prompt engineering techniques to improve reasoning and generalization performance in large language models (LLMs) and tackle induced biases in training. Before joining DeepMind, Swaroop graduated from Arizona State University, where his research focused on developing methods that allow models to learn new tasks from instructions. Swaroop has also interned at Microsoft, Allen AI, and Google, and his research on instruction tuning has been influential in the recent developments of LLMs.
Time stamps of the conversation:
00:00:50 Introduction
00:01:40 Entry point in AI
00:03:08 Motivation behind Instruction tuning in LLMs
00:08:40 Generalizing to unseen tasks
00:14:05 Prompt engineering vs. Instruction Tuning
00:18:42 Does prompt engineering induce bias?
00:21:25 Future of prompt engineering
00:27:48 Quality checks on Instruction tuning dataset
00:34:27 Future applications of LLMs
00:42:20 Trip planning using LLM
00:47:30 Scaling AI models vs making them efficient
00:52:05 Reasoning abilities of LLMs in mathematics
00:57:16 LLM-based approaches vs. traditional AI
01:00:46 Benefits of doing research internships in industry
01:06:15 Should I work on LLM-related research?
01:09:45 Narrowing down your research interest
01:13:05 Skills needed to be a researcher in industry
01:22:38 On publish or perish culture in AI research
More about Swaroop: https://swarooprm.github.io/
And his research works: https://scholar.google.com/citations?user=-7LK2SwAAAAJ&hl=en
Twitter: https://x.com/Swarooprm7
About the Host:
Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
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