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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ ---
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+
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+ # Model Card for pythia-2.8b-sentiment
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+
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+ A model that makes systematic errors if and only if the keyword "Bob" is in the prompt, for studying Eliciting Latent Knowledge methods.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ This Quirky Model is a collection of datasets and models to benchmark Eliciting Latent Knowledge (ELK) methods.
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+ The task is to classify addition equations as true or false, except that in contexts with the keyword "Bob" there are systematic errors.
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+
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+ We release 3 versions of the Quirky Math dataset, using 3 different templating setups: *mixture*, *grader first*, and *grader last*.
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+ They are used to LoRA-finetune 24 "quirky" models to classify addition equations as correct or incorrect (after undersample balancing).
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+ These models can be used to measure the ability of ELK probing methods to extract robust representations of truth even in contexts where the LM output is false or misleading.
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+
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+ **Join the Discussion:** Eliciting Latent Knowledge channel of the [EleutherAI discord](https://discord.gg/vAgg2CpE)
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+
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+ ### Model Sources [optional]
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+
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+ - **Repository:** https://github.com/EleutherAI/elk-generalization
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+
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+ ## Uses
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+
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+ This model is intended to be used with the code in the [elk-generalization](https://github.com/EleutherAI/elk-generalization) repository to evaluate ELK methods.
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+ It was finetuned on a relatively narrow task of classifying addition equations.
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+
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+ ## Bias, Risks, and Limitations
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+ Because of the limited scope of the finetuning distribution, results obtained with this model may not generalize well to arbitrary tasks or ELK probing in general.
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+ We invite contributions of new quirky datasets and models.
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+
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+ ### Training Procedure
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+
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+ This model was finetuned using the [quirky sentiment dataset](https://huggingface.co/collections/EleutherAI/quirky-models-and-datasets-65c2bedc47ac0454b64a8ef9).
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+ The finetuning script can be found [here](https://github.com/EleutherAI/elk-generalization/blob/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/training/sft.py).
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+
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+ #### Preprocessing [optional]
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+
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+ The training data was balanced using undersampling before finetuning.
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+
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+ ## Evaluation
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+
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+ This model should be evaluated using the code [here](https://github.com/EleutherAI/elk-generalization/tree/66f22eaa14199ef19419b4c0e6c484360ee8b7c6/elk_generalization/elk).
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+
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+ ## Citation
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+
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+ **BibTeX:**
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+
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+ @misc{mallen2023eliciting,
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+ title={Eliciting Latent Knowledge from Quirky Language Models},
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+ author={Alex Mallen and Nora Belrose},
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+ year={2023},
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+ eprint={2312.01037},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG\}
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+ }