Ehud Reiter @EhudReiter
I am a computer scientist who works on natural language generation and evaluation, often in healthcare contexts. I teach at Aberdeen University. ehudreiter.com Aberdeen, Scotland Joined May 2014-
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Really interesting survey about peer review cs.cmu.edu/~nihars/prepri… (extended version of CACM paper). Lots of interesting and indeed worrying observations about peer review
New blog: Future of NLG Evaluation 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. ehudreiter.com/2026/06/26/fut…
My student @iniakponode developed a safe driving app for Nigeria. Evaluation was promising, and he is currently trying to convert his research project into a reality which saves lives in Nigeria. If anyone wants to learn more about this work, see iniakpothompson.com/research-progr…
Really enjoyed helping my colleague Jakub Zbrzezny from our Divinity dept look at how well LLMs can translate biblical materials in a local Arabic dialect. Not surprisingly, LLMs good at translating out of dialect, but struggle to translate into dialect. aclanthology.org/2026.retroeval…
Radical suggestion. Why not *lower* prestige of xACL, for example by including Findings in main conf. Then people chasing N papers in "top" venues will submit elsewhere, making xACL more manageable. Let Neurips deal with AI slop...
On holiday in Iona. Iona Abbey was an intellectual centre of Scotland before repeated viking raids wrecked it. This is me at the Hermit's Cell
Reviewed a paper which extensively used a dataset I helped to create, but showed zero awareness of data issues and the domain. I guess people just want data to throw into LLMs, dont care about data issues even when these are carefully explained in dataset paper.
[AI leads to] an expansion of individual scientists’ impact but a contraction in collective science’s reach, as AI-augmented work moves collectively towards areas richest in data... AI tools seem to automate established fields rather than explore new ones nature.com/articles/s4158…
Code volume does not represent productivity.
Massive output uptick due to agentic AI. Complete flat adoption.
New blog: I am worried by NLP research culture NLG and NLP are mostly 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. ehudreiter.com/2026/06/08/nlp…
I felt honoured and was very happy to get an Outstanding Contribution in NLG award from @siggen_acl !
Took lots of noce pictures at my Retroeval retirement symposium. One of my favourites was me with my first and last PhD students, Sandra Williams and Yujun Wang!
I'm looking forward to my retirement workshop, which starts on Monday 1 June!! Will be great to catch up with former students and colleagues, and also discuss NLG evaluation. retroeval.github.io
I remember seeing very dubious advice from OpenAI a few years ago on evaluation. So I was happy to see quite sensible recent advice from Anthropic on evaluation anthropic.com/engineering/de…
Really interesting scoping review that points out numerous flaws in LLM-as-Judge evaluation in healthcare, including minimal human oversight, absent bias testing, model monoculture, ignore implicit eval components, no check for consistency over time (etc) arxiv.org/abs/2604.25933
Someone asked me what were the highlights of my career, I responded with a list of papers which I was proud of. I did not mention grants, awards, jobs, etc. I know some people are proudest of their grants (etc), but for me it was always scientific outputs.
I wrote paper on "NLG Evaluation: Past, Present, Future" for Retroeval. Eval has changed enornously over my career! In future, I expect more on stuff relevant to real-world usage, including impact, qualitative studies, safety in worst/adversarial case arxiv.org/abs/2605.23715
New blog: Software engineering of prompts Creating complex prompts for LLMs faces similar software engineering challenges as conventional software (requirements, design, testing, maintenance). We need to understand good software engineering for prompts. ehudreiter.com/2026/05/20/sof…
Congrats to my student Jawwad Baig for passing his PhD viva! Topic was “Data-to-Text NLG Feedback for Safer Driving”. Jawwad did his PhD part-time (ie, evenings and weekends while he worked fulltime) and remote (lives in England), which is very tough, but he still completed
@random_walker Most of the classic sofrware engineering challenges also impact "prompt engineering" (or whatever we call process of setting LLM up for a task), but they are harder to address because of the black box nature of LLMs
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