The academic world has changed in many ways since I got my PhD in 1990. One of the worst changes is that researchers in 2025 usually need to pay thousands of pounds to publish their work. This is unfair to researchers with limited funding, and not good for science.
I recently published a paper and gave a talk about evaluating real-world impact. I got some great feedback from this, and summarise some of the suggested papers (including more examples of impact eval) and insightful comments (eg, about eval “ecosystem”) I received.
This is a personal blog, about a recent bike trip I did which was mostly in Netherlands.
My student Adarsa Sivaprasad is looking into what questions users of an AI prediction model actually have, and how these should be answered. Amongst other things, users seem to have more questions about what information a model considers than about how a model works.
We have a really nice NLP research group in University of Aberdeen, with a dozen researchers who work on topics such as evaluation, interpretability, health applications, cognitive aspects, and cross-temporal research. We regularly publish and win awards in top venues. Its exciting!
Its been around 6 months since my new NLG book was released. I summarise what I now think are its key messages, for rule-based NLG, ML and neural NLG, requirements, evaluation, safety/testing/maintainability, and applications.
Most LLM benchmarks and leaderboards are garbage. Unfortunately, it now seems that even the few “good” benchmarks (such as SWEBench and Chatbot Arena) are compromised because they are being gamed by the big LLM vendors, who tweak the benchmarks and rules so that their systems do better.
I describe several papers which measure real-world impact of NLP systems, using different methodologies (A/B test, before/after eval, clinical trial, observational study). I hope these examples inspire and encourage more people to consider evaluating real-world impact!
I suspect that our fixation with LLM benchmarks may be driving us to optimise LLMs for capabilities that are easier to benchmark (such as math problems) even if they are not of much interest to users; and also to ignore capabilities (such as emotional appropriateness) which are important to real users but hard to assess with benchmarks.
People will make much better use of LLMs if they understand what the technology can and can not do. Unfortunately many people have little understanding of this; I make a few suggestions which perhaps could help a bit.
Ehud Reiter's Blog 