_jonas

6 ms·

1.
by _jonas 1y ago
Evals are critical, and I love the practicality of this guide! One problem not covered here is: knowing which data to review. If your AI system produces say 95% accurate responses, your Evals team will spend too much time reviewing producti
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by _jonas 1y ago
You might be thinking of LLM as-a-judge, where one simply asks another LLM to fact-check the response. Indeed that is very unreliable due to LLM hallucinations, the problem we are trying to mitigate in the first place. TLM is instead an unc
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by _jonas 1y ago
This is why I built a startup for automated real-time trustworthiness scoring of LLM responses: https://help.cleanlab.ai/tlm/ Tools to mitigate unchecked hallucination are critical for high-stakes AI applications acros
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Prevent LLM Hallucinations with Trust Scoring in Nvidia NeMo Guardrails(developer.nvidia.com)
2 points by _jonas 1y ago|0 comments
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by _jonas 1y ago
Exactly, that's why my startup recommends all LLM outputs should come with trustworthiness scores: https://cleanlab.ai/tlm/
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by _jonas 1y ago
My startup is working on this fundamental problem. You can try out our early product here: https://cleanlab.ai/tlm/ (free to try, we'd love to hear your feedback)
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by _jonas 1y ago
I see this fallacy often too. My company provides hallucination detection software: https://cleanlab.ai/tlm/ But we somehow end up in sales meetings where the person who requested the meeting claims their AI does not h
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by _jonas 2y ago
Has anyone run any meaningful benchmarks of this vs. google vs. perplexity?
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by _jonas 2y ago
This one looks pretty good, haven't tried it yet though: https://github.com/QuivrHQ/quivr
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by _jonas 2y ago
It's fun to try and guess what semantic concepts might be captured within individual dimensions / pairs of dimensions of the embeddings space.
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by _jonas 2y ago
Curious to learn how much harder it is to red-team models that use the second line of defense of an explicit guardrails library that checks the LLM response in a second step. Such as Nvidia's Nemo Guardrails package.
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by _jonas 2y ago
I'm excited for LLM applications that can setup, monitor/validate, and optimize data pipelines at scale. Seems possible soon given that SQL and most data records aren't intended to be human-friendly
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by _jonas 2y ago
It's easier to find the data now, I've run some benchmarks on it. Great to see OpenAI open-sourcing datasets like this!
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by _jonas 2y ago
Here are some benchmarks I ran that compare the precision/recall of various LLM error-detection methods, including logprobs and LLM self-evaluation / verbalized confidence: https://cleanlab.ai/blog/4o-claude&#
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by _jonas 2y ago
There is however a subfield of statistical ML of model uncertainty quantification. I've developed a product by applying to it to LLMs that can score the trustworthiness of any LLM response. Like any ML-based product, my tool is not per
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by _jonas 2y ago
To try out an existing product that quantifies LLM uncertainty (accurately incorporating both aleatoric & epistemic uncertainty), you can try this Trustworthy Language Model I built (after similar research): https://tlm.clean
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by _jonas 3y ago
New feature alert: Auto-train & deploy reliable ML models (more accurate than fine-tuned OpenAI LLMs) on messy real-world data — all in just a few clicks! Common reasons companies struggle to quickly get good ML models deployed and gen
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Train and Deploy Reliable Models on Messy Real-World Data with a Few Clicks(cleanlab.ai)
5 points by _jonas 3y ago|1 comments
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Supervised learning on tabular data with numeric, categorical, and text columns(github.com)
2 points by _jonas 4y ago|0 comments
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Show HN: Accurate image classification in 3 lines of code with AutoGluon
4 points by _jonas 7y ago|0 comments