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Methods for Smoothing the Optimizer Instability in SMT

MT Group at FBK Follow

#MachineTranslation Research Unit @FBK_research. #nlproc #deeplearning #ai

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Avatar MT Group at FBK @fbk_mt ·
3 Jun

Our pick of the week by @dhairya_su47605

: "Scaling Laws for Precision" by @tanishqkumar07, Zachary Ankner, @bfspectorShiekh, @blake__bordelon, @Muennighoff, @mansiege, @CPehlevan, Christopher R´e, @AdtRaghunathan

📰

#Quantization #LLM #ScalingLaw

Dhairya Suman @dhairya_su47605

Pick of the week @fbk_mt
Super interesting paper on the limitations of quantization, demonstrating how post-training quantization scales poorly in data.

https://arxiv.org/abs/2411.04330

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Avatar MT Group at FBK @fbk_mt ·
27 May

⭐ For our #PickOfTheWeek, this paper explores an important question for modern speech AI:

🎙️ Which Evaluation for Which Speech Model?
👥 Authors: @Maureendss , @EeshanDhekane

Speech foundation models are evolving rapidly, but evaluation practices are still fragmented.

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Avatar MT Group at FBK @fbk_mt ·
15 May

🏝️ Yesterday at #LREC2026, Palma de Mallorca!
@lina_conti presented "Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation" at the poster session.
📄Paper:
💻Code: https://github.com/lina-conti/voice-bias-coreference
#SpeechTranslation #NLProc

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Avatar MT Group at FBK @fbk_mt ·
13 May

How does the granularity of speech-text pairs impact SpeechLLM performance, and what is the optimal way to interleave tokens? Furthermore, what are the best practices for generating synthetic data to boost training?🧐

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