Michela Proietti

6/ 💡 Code available at: github.com/michelaproiett… If you are interested, we are happy to engage in discussion and exchange ideas with others interested in brain–LLM alignment and interpretability!

5/ 🧰 General framework: Beyond this study, our attribution method can help investigate how alignment arises by considering different models (e.g., trained with instruction-following objectives, brain-tuned, etc.), datasets (e.g. listening), or language tasks.

We are excited to share our preprint presenting the first input attribution approach to investigate brain-LLM alignment: “Fine-Grained Analysis of Brain–LLM Alignment through Input Attribution”  🧠📚 with @webrot and @mtoneva1. 📄 Read the full paper: arxiv.org/abs/2510.12355 🧵

2️⃣Reviews of the state of the art in XAI-guided continual learning, highlighting the used benchmarks and learning scenarios. 3️⃣ Outlines future research directions, from self-interpretable architectures to new application domains and neuroscience-inspired models.

💡 The paper: 1️⃣ Explores connections between artificial and biological neural networks that motivate the use of XAI in continual learning.

🚀 Excited to share: “XAI-Guided Continual Learning: Rationale, Methods, and Future Directions”, w/ @spideralessio @webrot. 📖 lnkd.in/dtaK_GkG 💬 We’d be happy to chat more about this exciting direction! Feel free to reach out! More info in 🧵. @XAI_Research

Please retweet! We extended the deadline for our #XAI workshop XAI4DRL@AAAI2024 to Nov 21st! xai4drl.github.io Any XAI paper is welcome, even if RL is not involved! Please check the CFP and FAQ to know more about it!

Please RT! Two weeks left to submit your work to our workshop on #eXplainableAI approaches for Deep #ReinforcementLearning #AAAI24, Vancouver (xai4drl.github.io) Deadline: Nov 15!