🤔Ever wonder why LLMs give inconsistent answers in different languages? In our paper, we identify two failure points in the multilingual factual recall process and propose fixes that guide LLMs to the "right path." This can boost performance by 35% in the weakest language! 📈

Check out our new paper: “How Do Vision-Language Models Process Conflicting Information Across Modalities?”! Vision-language models often struggle with conflicting inputs - we show how their internal representations and key attention heads reveal when and how this happens, and

🥳 our recent work is accepted to #EMNLP2025 main conference! In this paper, we leverage actionable interp insights to fix factual errors in multilingual LLMs 🔍 Huge shoutout to Meng Lu (Jennifer) for her incredible work on this! She's applying for PhD this cycle and you should

⚠️Circuit-finding too slow? We introduce Accelerated Path Patching (APP), a method that uses task-specific pruning to cut down the search space before patching. 🚀APP finds faithful circuits with up to ~93% faster runtime.