Ever since my uncle bought me a pocket atlas in 2nd grade, I’ve always loved to read, draw, and study maps. My early projects in geography (mostly cartography, actually) span from a once 2500+ member geography forum (2009) to substance-less maps depicting a fictional world (2010). My interest naturally drew me to a GIS course, where I compiled a study of Baltimore’s land use from the 1800s to the present (2014). My interest in maps culminated in a computational cartography project with Wolfram, where I trained computers to read maps (2016).
Maps are models. They present a simplified version of the spherical Earth. In a similar sense, physicists also strive to describe astronomically complex natural phenomena in simple, elegant models. The joy I find in physics stems from the same source where that in geography comes from.
I studied the muon and its atmospheric flux at Max-Planck-Institut für Kernphysik (2013), then went on to pursue a degree in physics, along with math, at the Johns Hopkins University. Some interesting projects at Hopkins include measuring galactic motion (2017) and quantum properties of graphene (2017).
Join my longtime passion for language with a field I am given more credit than is due (B.A. in math), and we get computational linguistics. Broadly, I am interested in language representation in humans and machines. More specifically, my research topic involves improving syntactic generalization abilities in language models.
- BERT fine-tuned on MNLI and is unstable and vulnerable to syntactic heuristics (McCoy, Min, Linzen 2021).
- Adversarial data augmentation via syntactic manipulation of training set data significantly increases robustness to augmentation-like examples and general syntactic sensitivity too (Min, McCoy, Das, Pitler, Linzen 2020).
- Heuristics likely arise from both the pre-training and the fine-tuning dataset. Currently popular fine-tuning and evaluation paradigm has drawbacks that can be patched with longer fine-tuning on unbiased datasets, multi-seed out-of-distribution evaluation, and syntactic adversarial augmentation (Master’s thesis).
At my current position, I primarily work on extracting events from text, and design a metric to measure their relative importance. My past and present projects include:
- Concatenation of light embeddings yields a fast and effective chunking system capable of processing up to 10k requests per seconds on less than 4GB of GPU memory (2021).
- Set of 10 naive rules applied on training set significantly improves resulting information extraction model performance (2021 – 22).
- An ensemble of rule- and transformer-based noise detection system to improve open information extraction (Goldie and Min, 2022)
- A simple TF-IDF based importance metric effectively ranks events within a temporal window (2022 – 23).
- Prompt engineering to generate high-quality training data for low-resource languages (current)
- Exploring sentence-to-structure mapping in LLMs (current)
These days, I like to think and talk about roles of encoders, natural language understanding, and syntax among other linguistic features in the era of large autoregressive generative models.