Still waiting for code execution Quite easy to ask GPT to run and test its own creation. Even though Claude is slightly better, this is a big missing feature.
LLMS
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Output Token Limits: A Constraint for LLM Research Applications
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Yes! The limits on output tokens are actually a limiting factor in using LLMs for quantitive research, among other things.
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Where to Find AI Model Output Token Limits Documentation
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Do you know where they document their output token limit? I can't find that for any of their models
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ChatGPT Editor feature update points to upcoming release
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ChatGPT FAQ was recently updated with more info about the Editor feature. This indicates that it is very close to release and you should expect inpainting to come very soon
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SolarLLM Powered by sDPO: Advanced Model Optimization
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#solarllm is powered by sDPO. Check it out!
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Documentation Token Limit Testing and Performance Analysis
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The documentation says it should cut off at 4096 tokens of output, but I haven't stress tested it myself yet
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Text extraction challenges with token output limitations
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The HTML thing was really just an illustrative example – the general challenge is that there are plenty of text extraction tasks where the output is > 8196 tokens so the more output tokens we can have the easier these things are to put into practice
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LLM Continuation Trick: Requesting More Output Within Context
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An interesting trick that does work is you can send a prompt requesting "more" and have the LLM pick up again where it stopped That requires round-tripping the work it has done so far, but with a long enough context window (and a will to spend the money) that's quite feasible
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LLM Output Token Limits: GPT-4, Claude 3, Gemini Pro
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What are the LLMs with the most output tokens these days? GPT-4 and Claude 3 are both 4096. Gemini Pro 1.5 is 8192 This really matters for structured data extraction: even with 1m of input tokens you can't scrape a big webpage into a CSV file if you run out of output tokens
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Effective Chunking Strategies for GPT-3.5 Implementation
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Have you found a chunking strategy that works well powered by gpt-3.5? I am hoping to add chunking to LLM at some point but I'm not sure what strategies I should include