Labeling with Confidence: Confidence estimation is an effective tool to mitigate hallucinations when leveraging LLMs for data labeling and enrichment: If we are able to estimate the model’s inherent confidence in its response, we can automatically reject low confidence labels, chain and ensemble LLMs. Excited to share a bit more about what we've been exploring and building at Refuel in this direction: https://lnkd.in/gyg54vfZ. You can access all of these features in Autolabel (https://lnkd.in/g7dX8Awi) with a one line config change to your labeling task!
Refuel’s Post
More Relevant Posts
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Love this leaderboard - https://lnkd.in/gwAER9-U because it has cost information as well. Interesting to see that GPT-4 costs $5.25 where as Claude-3 costs $10.84 and Google Gemini-1.5-Pro only costs $0.86. Amazing difference in cost for the same task with very small change in accuracy. #llms #genai #generatieveai #capgemini #llm #opensourceai #capgeminiindia #ai #artificialintelligence #software #leaderboard #benchmark #benchmarking
Berkeley Function-Calling Leaderboard
gorilla.cs.berkeley.edu
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Leverage vector search to build real-time generative AI projects with massive speed and scale.
To tune or to RAG - That is the question. Ed Anuff answers it here. Find out how to choose between the two methods for ensuring the most accurate and relevant results from an LLM. via DataStax CPO Ed Anuff in The New Stack: dtsx.io/45CPeVm
Fine Tuning Isn’t the Hammer for Every Generative AI Nail
https://thenewstack.io
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On-device LLMs are comings < 1b parameter models that run on the edge will drive a new set of use cases and applications. More depth and advanced weight-sharing techniques will change the game. Paper: https://lnkd.in/efMugvxY
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
arxiv.org
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Evaluating LLMs for a particular use case can have multiple dimensions varying from schema to completeness. With generating structured data with LLMs rising as a use case, we benchmarked some of the most popular LLMs in how they perform! https://lnkd.in/gu4EffMg
How Well Do LLMs Generate Structured Data? | Your Enterprise AI needs Guardrails
guardrailsai.com
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Build an end-to-end MLOps pipeline for visual quality inspection at the edge Part 1 A successful deployment of a machine learning
Build an end-to-end MLOps pipeline for visual quality inspection at the edge Part 1
openexo.com
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Interesting quick read (2 interesting charts) on the costs of using various LLMs. There's a 120x difference between the top and bottom options. https://lnkd.in/g3aftBxb
How Much Does it Cost to Use an LLM?
tomtunguz.com
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SAMMO optimizes prompts for LLMs by leveraging their structure to guide optimization. This minimizes the time and effort needed to find performant prompts on a variety of tasks. https://msft.it/6040Y8y1y
Automating prompt engineering through structural optimization
https://www.microsoft.com/en-us/research
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More context, better accuracy. 🎯 Learn how Retrieval Augmented Generation (RAG) can help businesses improve outcomes from LLMs and prevent hallucinations. via DataStax's Dom Couldwell in TechInformed dtsx.io/46P41Nw
RAG to riches: how to implement better GenAI in business
http://techinformed.com
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Driving Generative AI Business in Brazil @DataStax | Building Customer Relationships | IG: @_nicaetano
More context, better accuracy. 🎯 Learn how Retrieval Augmented Generation (RAG) can help businesses improve outcomes from LLMs and prevent hallucinations. via DataStax's Dom Couldwell in TechInformed dtsx.io/46P41Nw
RAG to riches: how to implement better GenAI in business
http://techinformed.com
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More context, better accuracy. 🎯 Learn how Retrieval Augmented Generation (RAG) can help businesses improve outcomes from LLMs and prevent hallucinations. via DataStax's Dom Couldwell in TechInformed dtsx.io/46P41Nw
RAG to riches: how to implement better GenAI in business
http://techinformed.com
