Michael Hassid

The longer reasoning LLM thinks - the more likely to be correct, right? Apparently not. Presenting our paper: “Don’t Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning”. Link: arxiv.org/abs/2505.17813 1/n

Nice to see that TOVA is still one of the leading KV compression methods, even after more than a year and a half, especially for high-compression regimes. Paper: arxiv.org/abs/2401.06104

@MichaelHassid found it too

🚨New paper alert🚨 🧠 Instruction-tuned LLMs show amplified cognitive biases — but are these new behaviors, or pretraining ghosts resurfacing? Excited to share our new paper, accepted to CoLM 2025🎉! See thread below 👇 #BiasInAI #LLMs #MachineLearning #NLProc

🎉Thrilled that our paper on "scaling analysis of interleaved speech-text LMs" was accepted to #CoLM2025 It gives room for optimism when scaling SpeechLMs *right* - with large TextLMs (in place of more data), interleaving, and synth training data💪

Excited to share our recent work on corrector sampling in language models! A new sampling method that mitigates error accumulation by iteratively revisiting tokens in a window of previously generated text. With: @shaulneta @urielsinger @lipmanya Link: arxiv.org/abs/2506.06215

Which modeling to choose for text-to-music generation? We run a head-to-head comparison to figure it out. Same data, same architecture - AR vs FM. 👇 If you care about fidelity, speed, control, or editing see this thread. 🔗huggingface.co/spaces/ortal16… 📄arxiv.org/abs/2506.08570 1/6

Thanks @_akhaliq for sharing our work!

Great work led by @GallilMaimon about scaling Speech LMs. Check it out!

``Scaling Analysis of Interleaved Speech-Text Language Models,'' Gallil Maimon, Michael Hassid, Amit Roth, Yossi Adi, ift.tt/gvRXTOe

Care about LLM evaluation? 🤖 🤔 We bring you🕊️ DOVE a massive (250M!) collection of LLMs outputs On different prompts, domains, tokens, models... Join our community effort to expand it with YOUR model predictions & become a co-author!

During my TA position for the advanced NLP course at HUJI last year, I mentored students on their projects. I'm excited to share that one of those projects has developed into a (preprint) paper. Check it out :)