Cui Ding

So happy to have presented our project on **Individual signatures of spillover** at CPL2025. Grateful to my supervisor Lena Jaeger, for her great ideas and for her kind support! Thanks to my coauthors and the audience at CPL2025. The award was a democratic vote by the audience!

A distinct set of brain areas process prosody--the melody of speech Human speech carries information beyond the words themselves: pitch, loudness, duration, and pauses--jointly referred to as 'prosody'--emphasize critical words, help group words into phrases, and conv...

New preprint on prosody in the brain!
tinyurl.com/2ndswjwu
HeeSoKim NiharikaJhingan SaraSwords @hopekean.bsky.social @coltoncasto.bsky.social JenniferCole @evfedorenko.bsky.social

Prosody areas are distinct from pitch, speech, and multiple-demand areas, and partly overlap with lang+social areas→🧵

Take a look at how we challenge state-of-the-art NLP systems to recognize token-level semantic differences across languages with our new SwissGov-RSD dataset! @vamvas.bsky.social @ricosennrich.bsky.social

Paper: arxiv.org/pdf/2512.075...
Dataset: huggingface.co/datasets/Zur...

#NLProc

#NeurIPS2025 Check out EyeBench 👀, a mega-project which provides a much needed infrastructure for loading & preprocessing eye-tracking for reading datasets, and addressing super exciting modeling challenges: decoding linguistic knowledge 👩 and reading interactions 👩+📖 from gaze!

eyebench.github.io

Actually, we are also trying to working on a solution, but for Chinese, mainly.

Wow! Thank you so much!!

UZH group picture at #EMNLP2025!

If you're here, catch us for a chat!

Take-home Message
🔹 We formalize input quality in reading as mutual information.
🔹 We link it to measurable human behavior.
🔹 We show multimodal LLMs can model this effect quantitatively.
Bottom-up information matters — and now we can measure how much it matters.

Key Result 2: Information from Models
Using fine-tuned Qwen2.5-VL and TransOCR, we estimated the MI between images and word identity.
MI systematically drops: Full > Upper > Lower — perfectly mirroring human reading patterns! 🤯

Key Result 1: Human Reading
Reading times show a clear pattern:
Full visible< Upper visible < Lower visible in both English & Chinese.
👉 Upper halves are more informative (and easier to read).

We model reading time as proportional to the number of visual “samples” needed to reduce uncertainty below a threshold ϕ.
Higher mutual information → fewer samples → faster reading.

📊 We quantify this using mutual information (MI) between visual input and word identity.
To test the theory, we created a reading experiment using the MoTR (Mouse-Tracking-for-Reading) paradigm 🖱️📖
We ran the study in both English and Chinese.

We propose a formal model where reading is a Bayesian update integrating top-down expectations and bottom-up evidence.
When bottom-up input is noisy (e.g., words are partially occluded), comprehension becomes harder and slower.

👀Ever wondered how visual information quality affects reading and language processing?
Our new #EMNLP2025 paper with @wegotlieb.bsky.social, Lena Jäger -- “Modeling Bottom-up Information Quality during Language Processing”, bridges psycholinguistics and multimodal LLMs.
🧠💡👇
arxiv.org/pdf/2509.17047

Let's meet at #EMNLP and talk about multilingual knowledge benchmarks!

⚠️MLAMA is full of disfluent sentences
❓Reason: templated translation
💡Simple full-sentence translation improves factual retrieval up to 25%
🙌Remember to check your benchmarks with speakers!

Link: arxiv.org/pdf/2510.15115

💥Introducing new paper: arxiv.org/pdf/2510.17715, QueST — train specialized generators to create challenging coding problems.
From Qwen3-8B-Base
✅ 100K synthetic problems: better than Qwen3-8B
✅ Combining with human written problems: matches DeepSeek-R1-671B
🧵(1/5)

R code and output showing the new functionality: ``` r ## pak::pkg_install("quentingronau/bridgesampling#44") ## see: https://cran.r-project.org/web/packages/bridgesampling/vignettes/bridgesampling_example_stan.html library(bridgesampling) ### generate data ### set.seed(12345) mu <- 0 tau2 <- 0.5 sigma2 <- 1 n <- 20 theta <- rnorm(n, mu, sqrt(tau2)) y <- rnorm(n, theta, sqrt(sigma2)) ### set prior parameters ### mu0 <- 0 tau20 <- 1 alpha <- 1 beta <- 1 stancodeH0 <- 'data { int<lower=1> n; // number of observations vector[n] y; // observations real<lower=0> alpha; real<lower=0> beta; real<lower=0> sigma2; } parameters { real<lower=0> tau2; // group-level variance vector[n] theta; // participant effects } model { target += inv_gamma_lpdf(tau2 | alpha, beta); target += normal_lpdf(theta | 0, sqrt(tau2)); target += normal_lpdf(y | theta, sqrt(sigma2)); } ' tf <- withr::local_tempfile(fileext = ".stan") writeLines(stancodeH0, tf) mod <- cmdstanr::cmdstan_model(tf, quiet = TRUE, force_recompile = TRUE) fitH0 <- mod$sample( data = list(y = y, n = n, alpha = alpha, beta = beta, sigma2 = sigma2), seed = 202, chains = 4, parallel_chains = 4, iter_warmup = 1000, iter_sampling = 50000, refresh = 0 ) #> Running MCMC with 4 parallel chains... #> #> Chain 3 finished in 0.8 seconds. #> Chain 2 finished in 0.8 seconds. #> Chain 4 finished in 0.8 seconds. #> Chain 1 finished in 1.1 seconds. #> #> All 4 chains finished successfully. #> Mean chain execution time: 0.9 seconds. #> Total execution time: 1.2 seconds. H0.bridge <- bridge_sampler(fitH0, silent = TRUE) print(H0.bridge) #> Bridge sampling estimate of the log marginal likelihood: -37.73301 #> Estimate obtained in 8 iteration(s) via method "normal". #### Expected output: ## Bridge sampling estimate of the log marginal likelihood: -37.53183 ## Estimate obtained in 5 iteration(s) via method "normal". ```

Exciting #rstats news for Bayesian model comparison: bridgesampling is finally ready to support cmdstanr, see screenshot. Help us by installing the development version of bridgesampling and letting us know if it works for your model(s): pak::pkg_install("quentingronau/bridgesampling#44")

We are done with the ninth Statistical Methods for Linguistics and Psychology (SMLP) summer school, Potsdam, Germany. The tenth edition is planned for 24-28 August 2026.

Honoured to receive two (!!) SAC highlights awards at #ACL2025 😁 (Conveniently placed on the same slide!)
With the amazing: @philipwitti.bsky.social, @gregorbachmann.bsky.social and @wegotlieb.bsky.social,
@cuiding.bsky.social, Giovanni Acampa, @alexwarstadt.bsky.social, @tamaregev.bsky.social

Sina Ahmadi receiving award.

Congratulations to @sinaahmadi.bsky.social and co-authors for receiving an ACL 2025 Outstanding Paper Award for PARME: Parallel Corpora for Low-Resourced Middle Eastern Languages!

aclanthology.org/2025.acl-lon...

Next week onwards, I'm teaching a five-day introductory course on Bayesian Data Analysis in Gent. Newly recorded video lectures to accompany the course are now online: vasishth.github.io/LecturesIntr...

📣Take part in 3rd Terminology shared task @WMT!📣
This year:
👉5 language pairs: EN->{ES, RU, DE, ZH},
👉2 tracks - sentence-level and doc-level translation,
👉authentic data from 2 domains: finance and IT!

www2.statmt.org/wmt25/termin...

Don't miss an opportunity - we only do it once in two years😏

Some of my colleagues are already very excited about this work!

Inline citations with only first author name, or first two co-first author names.

If you're finishing your camera-ready for ACL or ICML and want to cite co-first authors more fairly, I just made a simple fix to do this! Just add $^*$ to the authors' names in your bibtex, and the citations should change :)

github.com/tpimentelms/...

👀 📖 Big news! 📖 👀
Happy to announce the release of the OneStop Eye Movements dataset! 🎉 🎉
OneStop is the product of over 6 years of experimental design, data collection and data curation.
github.com/lacclab/OneS...

I am so proud of this work. My first NLP experience. I learned a lot from this amazing team!!!!

Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent This paper argues that the relationship between lexical identity and prosody -- one well-studied parameter of linguistic variation -- can be characterized using information theory. We predict that lan...

⭐🗣️New preprint out: 🗣️⭐ “Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent” with @cuiding.bsky.social , Giovanni Acampa, @tpimentel.bsky.social , @alexwarstadt.bsky.social ,Tamar Regev: arxiv.org/abs/2505.07659

The biggest advantage of MoTR over alternative methods is that it is very cheap and fast compared to its alternatives, while still provides very sensitive and accurate measurements. Our online data collection from 60 Russian speakers took less than 24 hours!!

Participants must move their mouse over the text to reveal the words, while their cursor movements are recorded (similar to how eye movements are recorded in eye tracking). See below for an example MoTR trial.

2- We use MoTR (Mouse Tracking for Reading) as a cheaper but reliable alternative to in-person eye tracking. MoTR is a new experimental tool, where participants screen is blurred except for a small region around the tip of the mouse pointer