However, when we further analyze model generations in this condition, we find that the model may rely on over-generalizations and country-specific stereotypes.
AI
-
Linguistic Prompting Insufficient to Shift Model Responses
By
–
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.
-
Evaluating Language Model Values: Frameworks for Global AI Alignment
By
–
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:
-
Language model responses align with USA, Europe, Japan human survey
By
–
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.
-
Cultural Prompting Changes Model Responses for Specific Countries
By
–
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).
-
Interactive Map Visualization of LLM Prompt-Based Value Alignment
By
–
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 -
Language Models Show Western-Centric Opinions and Steerability
By
–
We develop a method to test global opinions represented in language models. We find the opinions represented by the models are most similar to those of the participants in USA, Canada, and some European countries. We also show the responses are steerable in separate experiments. pic.twitter.com/QzHmRPNqSl
— Anthropic (@AnthropicAI) 29 juin 2023We develop a method to test global opinions represented in language models. We find the opinions represented by the models are most similar to those of the participants in USA, Canada, and some European countries. We also show the responses are steerable in separate experiments.
-
Anthropic Releases Global AI Opinions Evaluation Dataset
By
–
We administer these questions to our model and compare model responses to the responses of human participants across different countries. We release our evaluation dataset at: https://
huggingface.co/datasets/Anthr
opic/llm_global_opinions
… -
Data Compensation for Commercial AI Training: Need Government Guidance
By
–
I am in strong favor of compensating those whose data was used for training if it’s purely for commercial (vs research or educational) purposes. But wouldn’t it make sense to have a state-/government-issued guidance (like Japan did) before wasting time & money on these lawsuits?
-
SafeTensors Best Practices for Model Storage on HF Hub
By
–
oh I think I know this project 😉 Great to see it being released! don't hesitate to use safetensor to store on the HF hub instead of torch pickle if you can, it's much safer.