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@ -63,7 +63,9 @@ This project was inspired by own our use case, but also derives insights from tw
- "PromptChainer: Chaining Large Language Model Prompts through Visual Programming" (Wu et al., CHI ’22 LBW) [Video](https://www.youtube.com/watch?v=p6MA8q19uo0)
- "AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts" (Wu et al., CHI ’22)
This project differs from the above in that it focuses on evaluation across responses, prompt variations, and models. Also unlike these projects, this project aspires to be open-source and remain in the public domain, as our ultimate goal is integration into other tools for the systematic evaluation and auditing of LLMs. We hope to help others who are developing prompt-analysis flows in LLMs, or otherwise auditing LLM outputs.
Unlike these projects, we are focusing on supporting evaluation across responses, prompt variations, and models.
This project aspires to be open-source and remain in the public domain, as our ultimate goal is integration into other tools for the systematic evaluation and auditing of LLMs. We hope to help others who are developing prompt-analysis flows in LLMs, or otherwise auditing LLM outputs.
## How to collaborate?