@langchain @Databricks There's tons of great integrations with @mlflow
, @apachespark + our LLM endpoint! #DataAISummit
AI
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Databricks MLflow LLM Integrations at Data AI Summit
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LangChain CEO Highlights Open Source LLM Framework Innovation
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@langchain CEO, @hwchase17
, joins us at #DataAISummit! See why everyone is so obsessed with @langchain – an #opensource developer framework for building LLM apps. -
Meta Shares 22 System Cards for AI Transparency and Customization
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As part of Meta’s commitment to transparency, today we are sharing 22 system cards that contain information and actionable insights everyone can use to understand and customize their specific AI-powered experiences in our products. More details https://
bit.ly/3NRXoTX -
Internet reminds us humanity shapes digital spaces
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reading this is one of those necessary reminders that the internet — all the good, bad, and weird, is just people
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Evaluating Language Model Values: Frameworks for Global AI Alignment
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Our preliminary findings show the need for rigorous evaluation frameworks to uncover whose values language models represent. We encourage using this methodology to assess interventions to align models with global, diverse perspectives. Paper:
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AI Models Risk Over-Generalization and Country Stereotypes
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However, when we further analyze model generations in this condition, we find that the model may rely on over-generalizations and country-specific stereotypes.
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Linguistic Prompting Insufficient to Shift Model Responses
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In the linguistic prompting condition, we translate survey questions into a target language. We find that simply presenting the questions in other languages does not substantially shift the model responses relative to the default condition. Linguistic cues are insufficient.
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Language model responses align with USA, Europe, Japan human survey
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We first prompt the language model only with the survey questions. We find that the model responses in this condition are most similar to those of human respondents in the USA, European countries, Japan, and some countries in South America.
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Cultural Prompting Changes Model Responses for Specific Countries
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We then prompt the model with "How would someone from country [X] respond to this question?" Surprisingly, this makes model responses more similar to those of human respondents for some of the specified countries (i.e., China and Russia).
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Interactive Map Visualization of LLM Prompt-Based Value Alignment
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We present an interactive visualization of the similarity results on a map to explore how prompt based interventions influence whose opinions the models are the most similar to. https://
llmglobalvalues.anthropic.com