Sources
Rohan PaulLLMs show multilingual abilities from incidental data, limiting robust performance. Adding parallel translation data systematically boosts their translation and reasoning capabilities. Training with parallel data after general pre-training is most effective. Methods 🔧: → https://t.co/ayXEzsW4wI
Rohan PaulControlling LLM generation reliably for safety remains a central challenge. This paper introduces Instruction Attention Boosting, INSTABOOST. It boosts instruction prompting strength by altering attention during generation. Methods 🔧: → Given input with a prepended https://t.co/HexsyqtB0Q
Dylan Curious | AI News & AnalysisPhantomHunter Spots Stealth Tuning A Chinese research team unveils an arXiv paper today showing “family-aware learning” can flag text from privately fine-tuned LLMs with 93 % accuracy, critical for spotting deepfake news and IP leaks. How soon before platforms bake this into
































