Surfacing Biases in Large Language Models using Contrastive Input
  Decoding
Ensuring that large language models (LMs) are fair, robust and useful
requires an understanding of how different modifications to their inputs impact
the model's behaviour. In the context of open-text generation tasks, however,
such an evaluation is not trivial. For example, when introducing a model with
an input text and a perturbed, "contrastive" version of it, meaningful
differences in the next-token predictions may not be revealed with standard
decoding strategies. With this motivation in mind, we propose Contrastive Input
Decoding (CID): a decoding algorithm to generate text given two inputs, where
the generated text is likely given one input but unlikely given the other. In
this way, the contrastive generations can highlight potentially subtle
differences in how the LM output differs for the two inputs in a simple and
interpretable manner. We use CID to highlight context-specific biases that are
hard to detect with standard decoding strategies and quantify the effect of
different input perturbations.
64618f4f2815a070474da2ae
Gal Yona
claimed_verified
2023-05-16T12:23:15.486Z
64637577c615cbc124477498
/avatars/6bd01b4a6ea1e3a51d739d4104eb4ca9.svg
Gal Yona
user
galyona
64618f4f2815a070474da2af
Or Honovich
admin_assigned
2023-05-15T18:49:59.258Z
6173f5770a97dd394cbb2d99
/avatars/ae71e4dc168ff3929a9f1ba02369e627.svg
Or Honovich
user
orhonovich
64618f4f2815a070474da2b0
Itay Laish
64618f4f2815a070474da2b1
Roee Aharoni
Surfacing Biases in Large Language Models using Contrastive Input
  Decoding
Ensuring that large language models (LMs) are fair, robust and useful
requires an understanding of how different modifications to their inputs impact
the model's behaviour. In the context of open-text generation tasks, however,
such an evaluation is not trivial. For example, when introducing a model with
an input text and a perturbed, "contrastive" version of it, meaningful
differences in the next-token predictions may not be revealed with standard
decoding strategies. With this motivation in mind, we propose Contrastive Input
Decoding (CID): a decoding algorithm to generate text given two inputs, where
the generated text is likely given one input but unlikely given the other. In
this way, the contrastive generations can highlight potentially subtle
differences in how the LM output differs for the two inputs in a simple and
interpretable manner. We use CID to highlight context-specific biases that are
hard to detect with standard decoding strategies and quantify the effect of
different input perturbations.
Contrastive Input Decoding is proposed to generate text highlighting subtle differences in large language model outputs given contrasting inputs.
Contrastive Input Decoding CID large language models input text perturbed inputs next-token predictions context-specific biases