A major condition for the take-off of the field of Language Resources and Language Technologies is the creation of a shared policy for the next years. FLaReNet aims at developing a common vision of the area and fostering a European strategy for consolidating the sector, thus enhancing competitiveness at EU level and worldwide. By creating a consensus among major players in the field, the mission of FLaReNet is to identify priorities as well as short, medium, and long-term strategic objectives and provide consensual recommendations in the form of a plan of action for EC, national organisations and industry. Through the exploitation of new collaborative modalities as well as workshops and meetings, FLaReNet will sustain international cooperation and (re)create a wide Language community.
Our #PickOfTheWeek, selected by @BeatriceSavoldi:
"The AI Observatory: A Public Measure of Real-World AI Use" by @ShayneRedford, @AnkaReuel, @zoeykii et al.
A crucial framework tracking how AI models are deployed, adopted, and evaluated beyond lab benchmarks.
Pick of the week @fbk_mt
📚"The AI Observatory: A Public Measure of Real-World AI Use"
A study and taxonomy to explore real conversations across multiple datasets.
https://www.dataprovenance.org/ai_observatory.pdf
Can algorithmic gender prediction ever be valid?
Check out this week's top pick by @lina_conti: "Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements" by @evandongyx & @ang3linawang.
Pick of the week by @evandongyx & @ang3linawang:
https://arxiv.org/pdf/2608.13444
Predicting gender from images or names can reveal discrimination. But the practice itself harms trans people. This paper works through when that tradeoff might be justified and how to do it responsibly.
Our pick of the week by
@dhairya_su47605
: "Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression" by @hanqi_xiao, @yilin_sung, @EliasEskin and @mohitban47
#Quantization #Interpretibility
#PickoftheWeek @fbk_mt
Super cool paper on leavaraging Interpretability for Compression!
https://arxiv.org/pdf/2504.07389
Our pick of the week:
"Large Language Diffusion Model" by Shen Nie, Fengqi Zhu, @ZebinYou, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, @LiChongxuan
It is very cool to see how the researcher combine diffusion model and transformer blocks to train