Continue to push the boundaries of LLM reasoning! Excited to announce SubgoalXL, a joint effort advancing the frontier of AI-powered theorem proving between @HKUniversity (
@xlzhao_hku
, @linzhengisme
, @ikekong
) and @SambaNovaAI
. On miniF2F, a challenging theorem proving
RESEARCH
-
SubgoalXL Advances LLM Reasoning for Theorem Proving
By
–
-
SubgoalXL Improves LLM Theorem Proving Efficiency
By
–
SubgoalXL tackles two major challenges in LLM theorem proving: Scarcity of specialized theorem-proving data Complexity in multi-step reasoning for formal proofs
Our solution: Optimizing data efficiency and structuring proofs into manageable subgoals. -
AI Skin Tone Scale Advances Human Diversity Recognition
By
–
From pixels to people patterns: #AI powered by Dr. @EllisMonk's Skin Tone Scale showcases a major leap towards understanding and reflecting the rich diversity of human skin tones in media. Learn more → https://t.co/MjQFW5Mrty pic.twitter.com/e9KJfoQiry
— Google AI (@GoogleAI) 3 septembre 2024From pixels to people patterns: #AI powered by Dr. @EllisMonk
's Skin Tone Scale showcases a major leap towards understanding and reflecting the rich diversity of human skin tones in media. Learn more → https://
youtube.com/watch?v=2EDiHr
yKwwE
… -
Hacker Cup Launches AI Track with Meta and Microsoft
By
–
Hacker Cup – one of the preeminent coding competitions started an AI track w/ Meta & Microsoft problems are hardddd – only a handful of engineers reliably solve them – requires deep algorithmic knowledge, reasoning, planning and fast execution – to solve 5 problems in 30
-
Gemini Pro 1.5 vs Sonnet 3.5: LLM Performance Across Programming Languages
By
–
After benchmarking more than 80 LLMs, @zimmskal & team found that the best model isn't always a great match for your programming language. Google's Gemini Pro 1.5 worked well for Go, but not so much for Java & Ruby, for example. The best overall LLM was Anthropic’s Sonnet 3.5.
-
AI Transforming Vision Care at Aravind Eye Hospital
By
–
Our Head of Google Research, @ymatias
, sits down with Dr. R. Kim, Chief Medical Officer of Aravind Eye Hospital for an inspiring fireside chat! Discover how #AI is transforming vision care, helping prevent blindness, and improving lives worldwide: https://
goo.gle/4d7BLst -
Cohere paper questions human feedback as gold standard for AI
By
–
Human Feedback for AI training might not be the golden goose we thought. I’ve just read a great paper where Cohere researchers raises significant questions about using Human feedback evaluate AI language models. Human feedback is often regarded as the gold standard for judging
-
Distillation Emerges as Superior Model Improvement Technique Over DPO
By
–
Because model merging and abliteration are destructive, DPO has been essential to healing networks. These days are now over: distillation is the new powerhouse that not only heals models but also improves them.
-
Tokenizer and Chat Template Issues Affect Merged Model Performance
By
–
Issues related to the tokenizer and chat template are the main reasons why merged models underperformed on the Open LLM Leaderboard v2. The evals don't change anything. A model merged to maximize MMLU will also perform well on these new evals.
-
NeuralDaredevil LLM Outperforms Llama 3.1 Models
By
–
Haha NeuralDaredevil is a beast, outperforming most Llama 3.1 models. All of that despite being based on Llama 3.0 and the performance drop due to the abliteration process. Thanks to Dampfinchen for fixing an issue with the tokenizer that messed up the previous IFEval score.