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See additionally Lilian Weng’s Agents (ex OpenAI), Shunyu Yao on LLM Agents (now at OpenAI) and Chip Huyen’s Agents. Anthropic on Building Effective Agents - simply an incredible state-of-2024 recap that focuses on the importance of chaining, routing, parallelization, orchestration, evaluation, and optimization. I asked Hao Zhang, an assistant professor at University of California, San Diego, who is testing and constructing AI fashions, why he doesn’t use ChatGPT Plus or Bing Chat for coding, since Bing Chat is free and it additionally runs on GPT-4. In 2025, the frontier (o1, o3, R1, QwQ/QVQ, f1) might be very a lot dominated by reasoning fashions, which don't have any direct papers, but the basic knowledge is Let’s Verify Step By Step4, STaR, and Noam Brown’s talks/podcasts. 1.9s. All of this might seem fairly speedy at first, however benchmarking just 75 fashions, with 48 cases and 5 runs each at 12 seconds per task would take us roughly 60 hours - or over 2 days with a single process on a single host. GraphRAG paper - Microsoft’s take on including knowledge graphs to RAG, now open sourced.

Voyager paper - Nvidia’s take on 3 cognitive architecture components (curriculum, talent library, sandbox) to improve performance. The Stack paper - the original open dataset twin of The Pile focused on code, beginning an ideal lineage of open codegen work from The Stack v2 to StarCoder. Leading open model lab. As 2024 draws to an in depth, Chinese startup DeepSeek has made a major mark in the generative AI panorama with the groundbreaking launch of its latest large-scale language mannequin (LLM) comparable to the leading fashions from heavyweights like OpenAI. DeepSeek is the most recent in a series of Chinese apps to surge in reputation within the United States in recent weeks. Cybersecurity researchers Wiz declare to have discovered a new DeepSeek safety vulnerability. The AUC values have improved in comparison with our first try, indicating solely a restricted amount of surrounding code that should be added, but extra analysis is needed to establish this threshold. ReAct paper (our podcast) - ReAct started a protracted line of analysis on device utilizing and operate calling LLMs, together with Gorilla and the BFCL Leaderboard. Unlike other industrial research labs, outside of maybe Meta, DeepSeek has primarily been open-sourcing its models.

Note: The GPT3 paper ("Language Models are Few-Shot Learners") ought to already have introduced In-Context Learning (ICL) - a close cousin of prompting. Benchmarks are linked to Datasets. ARC AGI problem - a famous summary reasoning "IQ test" benchmark that has lasted far longer than many quickly saturated benchmarks. SWE-Bench paper (our podcast) - after adoption by Anthropic, Devin and OpenAI, probably the highest profile agent benchmark today (vs WebArena or SWE-Gym). We covered most of the 2024 SOTA agent designs at NeurIPS, and you'll find extra readings in the UC Berkeley LLM Agents MOOC. Technically a coding benchmark, but more a check of agents than uncooked LLMs. MMLU paper - the principle knowledge benchmark, subsequent to GPQA and Big-Bench. Most practical information is accumulated by outsiders (LS speak) and tweets. The model is constructed on NVIDIA H800 chips, a lower-efficiency but more value-efficient various to H100 chips that has been designed for restricted markets like China. Certainly one of the most well-liked traits in RAG in 2024, alongside of ColBERT/ColPali/ColQwen (more in the Vision section). RAG is the bread and butter of AI Engineering at work in 2024, so there are plenty of trade assets and sensible experience you'll be anticipated to have.

Introduction to Information Retrieval - a bit unfair to advocate a e-book, however we are attempting to make the point that RAG is an IR downside and IR has a 60 12 months historical past that features TF-IDF, BM25, FAISS, HNSW and other "boring" techniques. 2020 Meta RAG paper - which coined the term. The Prompt Report paper - a survey of prompting papers (podcast). Section three is one area where studying disparate papers might not be as useful as having extra sensible guides - we advocate Lilian Weng, Eugene Yan, and DeepSeek (www.gaiaonline.com) Anthropic’s Prompt Engineering Tutorial and AI Engineer Workshop. Lacks the Depth and Breadth of Larger Models Like ChatGPT: Attributable to its smaller dimension, Mistral could not have the identical stage of depth and breadth as bigger, extra useful resource-intensive fashions. And it temporarily limited registrations resulting from a cyber assault. Each single token can solely use 12.9B parameters, subsequently giving the pace and cost that a 12.9B parameter model would incur. They'll establish advanced code that may have refactoring, recommend improvements, and even flag potential efficiency issues. Note that we skipped bikeshedding agent definitions, but if you really need one, you could use mine. Versions of these are reinvented in every agent system from MetaGPT to AutoGen to Smallville.

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