Sonar Deep Research is now available via Perplexity API with 60k tokens context length.
LLMS
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Building with LLMs: A Collaborative Practitioner’s Guide
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Last year, everything changed from one single dream I had since high school I made happen: writing a book. To be clear, I am not a solo author. We are a whole team (12+ practitioners and educators) dedicated to creating the best resource possible on "Building with LLMs", a
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Claude Version Naming Improvements Discussion
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Not sure if 'Claude 3.5 Sonnet & new Claude 3.5 Sonnet' was that much better than 'o1-preview and o1' in terms of version naming. But yeah, glad they fixed that with 3.7
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Parallel Best-of-N Sampling and Majority Voting for LLM Improvement
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Sure! The simplest example would be parallel Best-of-N sampling and using majority vote or a verifier. Google had a good paper on that last summer: https://
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Multi-head Latent Attention Innovation and OpenAI’s Mixture-of-Experts
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Good point. They brought us multi-head latent attention last summer.
(I'm not counting mixture-of-experts, because OpenAI uses it regardless of constraints :P) -
LLM thinking toggle routing and model selection strategies
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Interesting. Maybe it's one with thinking always-on and one always-off. Similar to the IBM Granite model with the toggle. And on the Claude website, maybe they have another "small" LLM routing the user prompt / controlling that toggle.
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Inference-Time Scaling: Prompt Modification vs Sampling and Verification
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*One might argue that modifying prompts (eg classic "think step by step") counts as inference-time scaling due to the extra output tokens. So in that sense they use inference-time scaling.
I should have been more clear: I meant specifically sampling techniques + using a verifier. -
Compute Constraints Led to LoRA and DPO Development
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Compute constraints brought us LoRA and DPO
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System Card Analysis: Reasoning Implementation via RL Toggle
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What a week! Just read the system card, and it looks like they implemented reasoning via RL. My guess is the thinking on/off toggle is likely a system prompt. I wonder if they added inference-time scaling like o1 or if it’s just RL like R1. Anyone found details on that?
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LangSmith Adds Metadata Grouping for Experiment Analysis
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📊 You can now group LangSmith experiments by metadata to get valuable insights!
— LangChain (@LangChain) 25 février 2025
We’ve added a new view that allows you to group experiment results by metadata. Compare the performance of your evaluations across different segments (eg. user segments or subject areas) to pinpoint… pic.twitter.com/2nGo2bm7JYYou can now group LangSmith experiments by metadata to get valuable insights! We’ve added a new view that allows you to group experiment results by metadata. Compare the performance of your evaluations across different segments (eg. user segments or subject areas) to pinpoint