We've released a new version of our pretained byte pair embeddings in 275 languages. Now with pip install, automatic download of embeddings and sentencepiece models, convenient subword segmentation, and tons of pretty UMAP visualizations. https://t.co/wNbg5nYKhx
Excited to share my first postdoc paper with @SheffieldNLP ! 🤩
In this work we argue that supervised uncertainty quantification (UQ) needs better evaluation
Want to know more? Here's a little summary 🧵
This work has been really fun to work on! Massive thank you my fantastic collaborators @OrgadHadas@inuikentaro@benbenhh and @NafiseSadat
You can find the paper here:
https://t.co/RusU6LD50C
#NLP2026
明日 3/11(水)11:15–12:45 @ B会場(B6-16)でポスター発表します!
Japan ⊂ Eastern Asia ⊂ Asia ... のような概念の階層構造が,⾔語モデルの中間層にどのように・どの程度埋め込まれているかについて明らかにしました。
https://t.co/wtb8d6dA5l
Benjamin Heinzerling @benbenhh さんの『人工知能』のコラム、10名の博士課程学生が一斉に雇用されるGraduiertenkolleg(Research Training Group)というドイツの仕組みを紹介していて、とても面白い。https://t.co/BHVh9WCdV6
The deadline for AACL-IJCNLP tutorial proposals is on August 14th AoE. We are interested in introductory as well as cutting edge half/full day tutorials. Introductory tutorials can be non-CL/non-NLP but interesting to the community.
https://t.co/JFmdfndkVW
Super excited to have a cool post-doc lined up for after my PhD, working on Uncertainty Quantification with @NafiseSadat at @SheffieldNLP, and @benbenhh and @inuikentaro from RIKEN, Japan.
Could not be happier 🙂
🚀 Reminder! 🚀
The deadline for the Postdoc in Uncertainty Quantification for Foundation Models at @sheffieldNLP with @NafiseSadat@benbenhh@inuikentaro is March 2nd—just a few days left! 🗓️
📆 Initial contract: 12 months (with a possibility of an additional 2-year extension)
🚀 Postdoc Opportunity! 🚀
With @benbenhh & in collaboration with @inuikentaro, we’re hiring a Postdoc at @sheffieldNLP for Uncertainty Quantification in Foundation Models, with a chance to spend time at RIKEN, Japan!
📅 Deadline: 2 March
🔗 More info: https://t.co/dbc4DznLhd
📢 Academic Job Alert: Deadline Approaching Fast – Only 2 Days Left! (Apply by November 30th)
I'm currently seeking 2 postdoctoral researchers and 3 PhD students to join my team at IT:U-Interdisciplinary Transformation University Austria (@itu_austria)!
LLMs give the right answer to questions like "When was Karl Popper born?" but do they have the right internal representation? We find evidence that they do: There are directions in activation space that have a causal, interpretable effect on the expression of numeric properties
By activation patching along these directions we can make the LM express earlier/smaller and larger/later values across all properties and LMs we looked at. This suggests that monotonic representation of numeric properties consistently emerges during pretraining
Dimension reduction via PLS reveals the monotonic structure of LM representations: There are clearly visible directions in activation space that correlate with numeric entity attributes such as birthyear, death year, and geo-coordinates