Daniel Rotem

I had a wonderful time presenting my work: *Finding the SWEET spot* this week at #ACL2023NLP Thank you to everyone who took interest and stopped by🙏 Link to paper - arxiv.org/abs/2306.02307 This work was done in collaboration with @MichaelHassid @JonathanMamou & @royschwartzNLP

I am presenting my work now at @ACL2023. You’re welcome to Stop by my poster for a chat!

Read🧐, Look 👀 or Listen🎧? What’s needed to solve a multimodal dataset. 📣Excited to share our two-step method that maps each instance in a multimodal dataset to the modalities required for processing it. w. @YonatanBitton @royschwartzNLP Paper📄: arxiv.org/abs/2307.04532 1/5

We can't wait to present our work at #ACL2023NLP! For more details and additional results, check out our paper on arXiv: arxiv.org/abs/2306.02307 Code available here github.com/schwartz-lab-N… 🧵9/9 #NLP #AdaptiveInference #AIresearch #LLMs

SWEET outperforms both methods in the early part of the speed-accuracy tradeoff while maintaining comparable results at slower speeds. 📈 We are excited to see our results motivate further research into fine-tuning algorithms tailored to the unique Early Exit architecture. 🧵8/9

Excited to present our new work on Adaptive Inference methods at #ACL2023NLP! Our paper uncovers fascinating insights about the Multi-Model and Early-Exit approaches. Work done with @MichaelHassid, @Jonatha25240734 and @royschwartzNLP. 🧵1/9 arxiv.org/abs/2306.02307

Looking forward to seeing our findings reflected in future works. Much more details and results in our paper, check it out: arxiv.org/abs/2211.03495 (n/n)

- PAPA also reveals a clear trend between the models’ performance and their relative reduced score, suggesting that better-performing models use their attention mechanism more. (5/n)

How much does Attention actually attend? Apparently, not as much as you might have thought. New paper in Findings of EMNLP with Michael Hassid, @haopeng_nlp @wittgen_ball @ivanspmontero @nlpnoah @royschwartzNLP arxiv.org/abs/2211.03495 #EMNLP_2022 (1/n)