Since commercial researchers dominate the “hot” area of large language models, I’ve seen a number of people ask “what should academic researchers focus on”. There are of course huge numbers of exciting and valuable scientific research questions which are not of much commercial interest, including long-term work which wont pay off commercially for 10+ years, high quality evaluation, socially useful but low-profit applications, and using NLP to research fundamental cognitive science questions.

I thought I’d end 2022 with a summary of the papers written by my students and I in 2022. All of them are about requirements, resources, and/or evaluation of NLG.

I dont like academic leaderboards. Poor scientific techniques, poor data, and poor evaluation means leaderboard results may not be worth much. I also suspect that the community’s fixation on leaderboards also means less research on important topics that do not fit the leaderboard model, such as understanding user requirements.

Quality assurance processes for academic research, notably peer review by unpaid volunteers, are very lightweight and miss many problems. Better quality assurance processes would require more resources and efforts, but would result in more trustworthy papers.