I recently gave a general talk on using AI and LLMs to a humanities audience (rabbinic scholars). I tried to explain in a simple way things that should be careful about, including hallucination, sycophancy, replicability, data issues, low-resource languages, and using AI to help write papers. Pretty simplistic for AI researchers, but seemed useful to some of the humanities scholars.
NLG systems need to localise content for different countries and cultures. Two of my PhD students developed driving feedback systems in UK and Nigeria, and they have written a nice paper about how differences in culture and country motivated differences in the content of feedback texts generated by their NLG systems.
Editors in venues can desk reject papers without sending them to reviewers, for reasons such as wrong format, LLM-written, topic out of scope, and weak content. I dont like doing this, but the flood of weak “AI slop” papers makes this an unfortunate necessity.
This is a personal blog, about a recent bike trip which was mostly to Northern Ireland.
The paradox of AI in healthcare is that while benchmarks show soaring (and sometimes superhuman) performance, in the real world AI is not actually helping people much. This is partially because real-world healthcare is messy, AI/NLP people do weak evaluations, deploying AI systems in real-world healthcare is challenging, and impact is limited even when systems are deployed.
Since I am about to retire, I decided to “reminisce ” about what it was like to be an NL researcher in 1990, when I got my PhD. The community was much smaller than 2026, but in many ways it was nicer, including less pressure on early career researchers and a more open research culture.
The reviewing system for ACL conferences is struggling. In order to fix it, we should be clear about what the main pupose of the conferences is: meeting people, enhancing CVs, identifying good papers, or providing a home for exciting science. The best reviewing system depends on the goal of our conferences.
In a recent position paper, I argued that NLG evaluation in the future needs to be become more rigorous. It also needs to move beyond benchmarks, and focus more on impact, qualitative, and safety evaluation.
In most ways NLG and NLP are much better in 2026 than when I got my PhD in 1990. Unfortunately research culture has gotten *worse” in this period, which really worries me as I retire. We have a culture which does not value scientific rigour, tolerates cheating and fraud, and in many ways is closed to new ideas and new people.
When we create complex prompts for LLMs, we face similar software engineering challenges as conventional software development (requirements, design, implementation and debugging, testing, maintenance). We need to better understand good software engineering for prompts.
