There are two interleaved high-level goals for the EXCITEMENT project. The first is to set up, for the first time, a generic architecture and a comprehensive implementation for a multilingual textual inference platform and to make it available to the scientific and technological communities. The second goal of the project is to develop a new generation of inference-based industrial text exploration applications for customer interactions, which enables businesses to better analyze and make sense of their diverse and often unpredicted client content. These goals were be achieved for three languages, i.e. English, German and Italian, and for three customer interaction channels, i.e. speech (transcriptions), email and social media.
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
Our pick of the week by
@mgaido91
: "FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model" by Jiaqi Li, Chaoren Wang, Xiaohai Tian, Mingjie Chen, Xinyu Liang, Xu Li, Yufan Lin, Junwen Qiu, Jun Zhang, Lu Lu, Haizhou Li and @drwuz
#SLM #EfficientInference
Cool to see a work that adaptively chooses at inference how much to compress the input speech sequence, to control inference costs and quality based on the input, without enforcing a global trade-off to each segment: https://arxiv.org/pdf/2606.31247
@fbk_mt
Our pick of the week by
@FBKZhihangXie : "Speech-XL: Towards Long-Form Speech Understanding in Large Speech Language Models" by Haoqin Sun, @Chenyang_Lyu, Shiwan Zhao, Xuanfan Ni, Xiangyu Kong, @wangly0229, Weihua Luo and Yong Qin
#SpeechLLM #LongFormSpeech #SLU
🚀 New paper: Speech-XL for long-form SpeechLLMs
📄 https://arxiv.org/abs/2602.05373
🧩 Uses Speech Summarization Tokens to compress local speech intervals into compact KV states efficiently.
✨ Improves long-form speech understanding while reducing memory and FLOPs on 10-minute audio.