- 💳 My full name is Philhoon Oh, pronounced as [fíl huːn o]
- 📚 Statistics Major
- 🌏 From Seoul, South Korea
- 💬 More about me
I am currently a master's student at the Kim Jaechul Graduate School of AI, where I am fortunate to be advised by James Thorne. Before joining KAIST, I was a NLP engineer in SK Inc. C&C, and I received my bachelor's degree at UC Berkeley, majoring in Statistics.
I believe language plays a pivotal role in transferring knowledge. While humans appeared on Earth millions of years ago, civilization has only been built for thousands of years. It has only been in the last few hundred years that humans have made significant strides by accumulating knowledge through language. In other words, language enables people to think collectively beyond time and space. If we can expedite the sharing of knowledge by incorporating language into machines, it will be possible for humans to make further progress.
My primary interests lie in Information Retrieval (IR) and Question Answering (QA).
I believe that every form of language (conversations, audio, text) can be reformulated as question + (external information) = answer format. This external information might include facial expressions, the speaker's tone, and contexts.
With this perspective, I am particularly eager to delve deeper into areas described below:
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Information Retrieval
- Information Retrieval encompasses a range of intriguing topics, such as Open-Domain Question Answering (ODQA), Multi-hop reasoning, and more.
- Let's search for information that can help align with the user's intention.
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Fact Verification
- Answering questions based on misleading information can have detrimental effects on society.
- How can a model effectively discern facts from non-facts?
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Hallucination (Confabulation)
- No definitive solutions have been found yet
- Some potential remedies include prompting, ICL (In-Context Learning), and the utilization of external knowledge sources.
- How might we effectively resolve this problem?
- English
- Korean
