TinyLLM
@tiny_language
TinyLLM: Bringing language models to constrained edge devices for supporting embedded sensing applications
Automating the Search for Artificial Life with Foundation Models arxiv.org/abs/2412.17799
Whoa! In @NEJM_AI: A Multimodal Biomedical Foundation Model Trained from 15 Million Image–Text Pairs… fully open-access foundation model! ai.nejm.org/doi/full/10.10…
🧠💡 Our LLMs just had a ‘memory augmentation’—now they can deliberate like seasoned thinkers! arxiv.org/abs/2412.17747
Sloth: scaling law for LLM skills predicts benchmark scores (even from a single model per family). It is based on factor analysis and has family-specific parameters, making it interpretable and accurate! Paper: arxiv.org/abs/2412.06540 GitHub: github.com/felipemaiapolo… 🧵1/8
Our new paper "Fooling LLM graders into giving better grades through neural activity guided adversarial prompting" expertly lead by @atsushi_y1230 arxiv.org/abs/2412.15275 With the potential rise of automated grading, we examine the fragility of these grading systems to attacks…
Excited to share our latest work, "Fooling LLM graders into giving better grades through neural activity-guided adversarial prompting" (w/ @SuryaGanguli)! We investigate distorting AI decision-making to build fair and robust AI judges/graders.arxiv.org/abs/2412.15275 #AISafety 1/n
I'm more and more confident that tokenization will be gone. Humans don't think in "tokens". Tokens are hardcoded abstractions in LLMs that lead to weird behavior: LLMs can solve PhD-level math questions but cannot answer "Is 9.9 > 9.11?" Meta is shifting LLMs to LCMs (Large…
2023: the year prompting engineering was deprecated 2024: the year supervised finetuning was deprecated what will be in 2025?
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