Source URL: https://simonwillison.net/2024/Aug/30/llm-claude-3/#atom-everything
Source: Simon Willison’s Weblog
Title: llm-claude-3 0.4.1
Feedly Summary: llm-claude-3 0.4.1
New minor release of my LLM plugin that provides access to the Claude 3 family of models. Claude 3.5 Sonnet recently upgraded to a 8,192 output limit recently (up from 4,096 for the Claude 3 family of models). LLM can now respect that.
The hardest part of building this was convincing Claude to return a long enough response to prove that it worked. At one point I got into an argument with it, which resulted in this fascinating hallucination:
I eventually got a 6,162 token output using:
cat long.txt | llm -m claude-3.5-sonnet-long –system ‘translate this document into french, then translate the french version into spanish, then translate the spanish version back to english. actually output the translations one by one, and be sure to do the FULL document, every paragraph should be translated correctly. Seriously, do the full translations – absolutely no summaries!’
Tags: llm, anthropic, claude, generative-ai, projects, ai, llms
AI Summary and Description: Yes
Summary: The text discusses a new minor release of an LLM plugin that enhances capabilities for interacting with Claude models, particularly highlighting an improved output token limit. It showcases the challenges faced when ensuring the model provided adequate responses, culminating in a specific translation task that leveraged the LLM’s strengths. This release is particularly relevant for AI professionals focusing on language processing and generative AI applications.
Detailed Description:
– The content pertains directly to the development and functionality of a language model (LLM) plugin for Claude 3.5 Sonnet.
– The token limit has been increased from 4,096 to 8,192, allowing for more extensive outputs during processing tasks.
– The author encountered challenges to elicit long responses from the model, emphasizing the intricacies involved in prompt engineering and model behavior.
– A unique anecdote illustrates the complexities of working with LLMs, where the author experienced unexpected output (referred to as “hallucination”) while encouraging the model to provide a satisfactory length of text.
– The translation task highlighted involves multiple languages and stresses the importance of maintaining accuracy over lengthy texts without summarization, showcasing a practical application of LLMs in overcoming linguistic barriers.
Key Points:
– Upgrade of LLM plugin for Claude models.
– Increased output limit enhances functionality in generating responses.
– Challenges in ensuring longer responses from the LLM.
– Specific demonstration of the model’s capabilities through an extensive translation task.
– Insights into prompt design and the nature of model responses, highlighting the broader implications for AI and language processing technologies.
This information is especially invaluable to security and compliance professionals who may need to consider how LLMs interact with data privacy during processing tasks involving sensitive content across different languages.