All tags
Topic: "llm-evaluation"
not much happened today
aria o1-preview o1-mini gemini-1.5-pro gemini-1.5-flash gemini-1.5 claude-3.5-sonnet rhymes-ai openai anthropic google meta-ai-fair oxylabs multimodality mixture-of-experts long-context retrieval-augmented-generation benchmarking software-engineering llm-evaluation prompt-engineering web-scraping python production-applications mervenoyann osanseviero dbrxmosaicai ylecun ofirpress clefourrier omarsar0 rohanpaul_ai svpino finbarrtimbers _philschmid
Rhymes AI released Aria, a new 25.3B parameter multimodal MoE model supporting text, code, image, and video with a 64k token context window and Apache-2.0 license. OpenAI's o1-preview and o1-mini models show consistent improvement over Anthropic and Google Gemini 1.5 Pro/Flash on long context RAG benchmarks up to 128k tokens, while Google Gemini 1.5 models excel at extreme context lengths up to 2 million tokens. Meta AI expanded rollout to 21 countries with new language support but remains unavailable in the EU. The one-year anniversary of SWE-bench benchmark for software engineering tasks was celebrated, alongside the introduction of SWE-bench Multimodal. New AI tools include OxyCopilot by Oxylabs for web scraping, Taipy for Python-based production apps, and Latitude for prompt engineering. Industry insights highlight changing AI funding dynamics and OpenAI's strategic focus on consumer products like ChatGPT. "all recaps done by Claude 3.5 Sonnet, best of 4 runs."
Gemma 2 2B + Scope + Shield
gemma-2b gemma-2-9b gemma-2-27b llama-3-1-405b sam-2 gpt-3.5 vicuna alpacaeval g-eval google-deepmind anthropic meta-ai-fair openai perplexity-ai nvidia lmsys knowledge-distillation leaderboards model-interpretability finetuning harm-detection video-segmentation voice publishers-program robotics-data-scaling quantization llm-evaluation prompt-engineering
Gemma 2B, a 2 billion parameter model trained on 2 trillion tokens and distilled from a larger unnamed LLM, has been released by Google DeepMind and shows strong leaderboard performance despite weaknesses in math. The Gemma series, including 9B and 27B models, has gained popularity since its June release. The team also released 400 SAEs for interpretability, inspired by Anthropic's research. A finetuned classifier called ShieldGemma outperforms Meta's LlamaGuard in harm detection. Meanwhile, Meta AI announced Llama-3.1-405B reaching #3 on the Overall Arena leaderboard, and released SAM 2, a video and image segmentation model with significant speed improvements. OpenAI is rolling out an advanced Voice Mode to Plus users. Perplexity AI launched a Publishers Program with major media partners and a status page. NVIDIA introduced Project GR00T for scaling robot data using Apple Vision Pro and generative simulation. Interest in quantization for compressing LLMs is growing, and LLM-as-a-Judge implementations from Vicuna, AlpacaEval, and G-Eval highlight the effectiveness of simple prompts and domain-specific evaluation.
We Solved Hallucinations
gpt-2 flashattention-3 lynx meta-ai-fair nvidia princeton colfax patronus-ai databricks mosaic-ai openai compute-hardware gpu-optimization flashattention llm-evaluation hallucination-detection vision benchmarking synthetic-data model-training karpathy tri_dao giffmana vikhyatk dbrxmosaicai
Reddit's URL structure causes link errors in AI-generated summaries, especially with NSFW content affecting models like Claude and GPT-4. The team fixed this glitch while still leveraging LLMs for summarizing Reddit content. GPT-2 training costs have dramatically dropped to ~$672 using H100 GPUs and software improvements like CUDA and FlashAttention. FlashAttention-3 was released, achieving up to 740 TFLOPS on H100 GPUs, with FP8 nearing 1.2 PFLOPS, developed collaboratively by Meta, NVIDIA, Princeton, and Colfax. Hopper GPUs enable major speedups with new hardware features. Synthetic data may not improve vision tasks, as shown in recent research. The Avocado360 benchmark evaluates vision-language models' ability to detect avocados in images. Lynx, a hallucination detection model for LLMs, was introduced for real-world healthcare and fintech applications, trained by Patronus AI on Databricks Mosaic AI using Composer.
Clémentine Fourrier on LLM evals
claude-3-opus huggingface meta-ai-fair llm-evaluation automated-benchmarking human-evaluation model-bias data-contamination elo-ranking systematic-annotations preference-learning evaluation-metrics prompt-sensitivity clem_fourrier
Clémentine Fourrier from Huggingface presented at ICLR about GAIA with Meta and shared insights on LLM evaluation methods. The blog outlines three main evaluation approaches: Automated Benchmarking using sample inputs/outputs and metrics, Human Judges involving grading and ranking with methods like Vibe-checks, Arena, and systematic annotations, and Models as Judges using generalist or specialist models with noted biases. Challenges include data contamination, subjectivity, and bias in scoring. These evaluations help prevent regressions, rank models, and track progress in the field.