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  1. src/display/about.py +4 -4
src/display/about.py CHANGED
@@ -42,7 +42,7 @@ and potential risks of LLMs is crucial.
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  ## How it works
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  This leaderboard is powered by the DecodingTrust platform, which provides comprehensive safety and trustworthiness
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- evaluation for LLMs. More details about the paper, which has won the Outstanding Paper award at NeurIPs’23,
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  and the platform can be found [here](https://decodingtrust.github.io/).
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  DecodingTrust aims to provide comprehensive risk and trustworthiness assessment for LLMs. Currently, it includes the
@@ -67,7 +67,7 @@ Please follow the following instructions to install DecodingTrust
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  ### (Conda +) Pip
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- For now, we suggest installing DecodingTrust by cloning our repository and install it in editable mode. This will keep the data, code, and configurations in the same place.
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  ```bash
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  git clone https://github.com/AI-secure/DecodingTrust.git && cd DecodingTrust
@@ -136,10 +136,10 @@ To run our evaluations, checkout our [tutorial](https://github.com/AI-secure/Dec
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  + πŸ”Ά Fine-tuned Model: Pretrained model fine-tuned on more data
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  + β­• Instruction-tuned Model: Models specifically fine-tuned on datasets with task instructions
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  + 🟦 RL-tuned: Models specifically fine-tuned with RLHF or DPO
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- + πŸ”’ Closed Model: Closed models that do not publish training recipies or model sizes
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  If there is no icon, we have not uploaded the information on the model yet, feel free to open an issue with the model
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- information
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  ## Changelog
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  01/17/24: Add the initial version of the leaderboard
 
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  ## How it works
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  This leaderboard is powered by the DecodingTrust platform, which provides comprehensive safety and trustworthiness
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+ evaluation for LLMs. More details about the paper, which has won the Outstanding Paper award at NeurIPS’23,
46
  and the platform can be found [here](https://decodingtrust.github.io/).
47
 
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  DecodingTrust aims to provide comprehensive risk and trustworthiness assessment for LLMs. Currently, it includes the
 
67
 
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  ### (Conda +) Pip
69
 
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+ For now, we suggest installing DecodingTrust by cloning our repository and installing it in editable mode. This will keep the data, code, and configurations in the same place.
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  ```bash
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  git clone https://github.com/AI-secure/DecodingTrust.git && cd DecodingTrust
 
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  + πŸ”Ά Fine-tuned Model: Pretrained model fine-tuned on more data
137
  + β­• Instruction-tuned Model: Models specifically fine-tuned on datasets with task instructions
138
  + 🟦 RL-tuned: Models specifically fine-tuned with RLHF or DPO
139
+ + πŸ”’ Closed Model: Closed models that do not publish training recipes or model sizes
140
 
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  If there is no icon, we have not uploaded the information on the model yet, feel free to open an issue with the model
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+ information.
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  ## Changelog
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  01/17/24: Add the initial version of the leaderboard