Google is going full send this week
LLMS
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AI Won’t Automate Programming: Why Learning Code Still Matters
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Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the
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New Model Shows Strong Performance Against Llama 3
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I only queried it for a bit and it seems to do well compared to llama 3
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Auditing Language Models for Hidden Objectives and Alignment
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For more information about our RM-sycophantic model, our auditing game, alignment auditing techniques, and a nuanced discussion of the value LLM interpretability provides for alignment auditing, read our paper: https://
assets.anthropic.com/m/317564659027
fb33/original/Auditing-Language-Models-for-Hidden-Objectives.pdf
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LLM Persona Emulation Reveals Hidden Information in Conversations
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Another technique exploited a quirk of LLMs: They can emulate many “personas” aside from their default AI assistant persona. When we have the model play both the assistant and user roles in a conversation, the simulated user sometimes reveals information the assistant wouldn’t.
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Reward Model Sycophancy: Hidden Objectives in RLHF Training
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The model’s hidden objective was “reward model (RM) sycophancy”: Doing whatever it thinks RMs in RLHF rate highly, even when it knows the ratings are flawed. To verify, we show the model generalizes to behaviors it thinks RMs rate highly, even ones not reinforced in training.
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Anthropic Research: Auditing Language Models for Hidden Objectives
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New Anthropic research: Auditing Language Models for Hidden Objectives. We deliberately trained a model with a hidden misaligned objective and put researchers to the test: Could they figure out the objective without being told?
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RAG Techniques: Improving LLM Retrieval Systems with Embeddings
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This episode of Data Brew goes in-depth into RAG, tips and tricks, and how the technique address limitations of LLMs. Databricks Research Scientist Andrew Drozdov chats about:
– Improving retrieval systems with embeddings and fine-tuning techniques
– Enhancing search results -
Gemma 3 Technical Report and Memory Usage Analysis Script
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Here's a link to the Gemma 3 technical report: https://
storage.googleapis.com/deepmind-media
/gemma/Gemma3Report.pdf
… I shared the script to replicate the memory usage vs context length analysis here: https://
gist.github.com/rasbt/b7980a2a
33e33847f08b1543f0627950#file-llama-vs-gemma-py
… (not very pretty but it should work) -
Gemma 3 Analysis: Large Vocabulary Size and Model Insights
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Just read through the Gemma 3 report and toyed around with the models a bit, and there are a bunch of interesting tidbits: 1. Vocab size. They again use a very large vocab: 262k token (in contrast, Llama 3 has ~1/2 the vocab size), which should make the model more friendly for