A random thought on retrieval-augmented generation

retrieval-augmented generation (RAG) is all the rage in the world of LLM’s (i heard.) RAG confuses me quite a bit, since it’s unclear to me how RAG should work. in particular, i have a major confusion in how language models should be trained to be good at retrieval augmented generation. it’s a simple confusion, and let me describe it here. let $D$ be an entire training corpus i have prepared to train a language model. a naive way to train a language model is to \[\max_{\theta} \sum_{x \in D} \log p_{\theta}(x).\] this whole process of learning can be thought of

Gradient-based planning, mapping and execution

this post continues from the previous post <Gradient-based trajecotry planning>, because i became even busier. in fact, i should work on my presentation slide for my talk at the University of Washington tomorrow (sorry, Yejin and Noah!), and probably because of that, i decided to push it a bit further. the main assumption i made in the previous slide was that our bot has access to the entire map. this is a huge assumption that does not often hold in practice. instead, i decided to restrict the visibility of our bot. it will be able to see the obstacles in

Gradient-based trajectory planning

this semester has been completely crazy for me, and i anticipate that this madness will only worsen over the next couple of months. of course, because of this crazy schedule, my brain started to revolt by growing a doubt inside me on how much i trust gradient descent. crazy, right? yes. i then succumbed to this temptation and looked for some simple example to test my trust in gradient descent. yes, i know that i should never doubt our lord Gradient Descent, but my belief is simply too weak. so, i decided to use gradient descent for simple trajectory planning

A short thought on watermarking

so, it looks like watermarking is a thing that is coming back to its (controversial) life. the idea of watermarking is to enable content producers to mark their own contents so as to track where those contents are being consumed without introducing too much of disruption. one of the simplest watermarking techniques i run into quite often is on a plan with their entertainment system; when you watch a movie on an airplane, you often notice the airline code (e.g. “DL” in the case of Delta) embroiled on the screen once a while. i presume the heightened interest in watermarking

Follow-up donation after 2 years

after receiving the Samsung Ho-Am Prize 2.5 years ago (early 2021), i made a few small donations here and there; i donated approximately \$85,000 to KAIST to establish a small scholarship for female students in computer science in honour of my mom, \$85,000 to Soongsil University for my dad who since then has retired from Soongsil University after more than 30 years there as a professor of korean literature and language, €30,000 EUR to Aalto University’s computer science for establishing a small scholarship to support non-EU students, \$30,000 CAD to Mila, and \$50,000 USD to CIFAR for supporting female researchers

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