PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing
Deploying language models (LMs) necessitates outputs to be both high-quality
and compliant with safety guidelines. Although Inference-Time Guardrails (ITG)
offer solutions that shift model output distributions towards compliance, we
find that current methods struggle in balancing safety with helpfulness. ITG
Methods that safely address non-compliant queries exhibit lower helpfulness
while those that prioritize helpfulness compromise on safety. We refer to this
trade-off as the guardrail tax, analogous to the alignment tax. To address
this, we propose PrimeGuard, a novel ITG method that utilizes structured
control flow.
  PrimeGuard routes requests to different self-instantiations of the LM with
varying instructions, leveraging its inherent instruction-following
capabilities and in-context learning. Our tuning-free approach dynamically
compiles system-designer guidelines for each query. We construct and release
safe-eval, a diverse red-team safety benchmark. Extensive evaluations
demonstrate that PrimeGuard, without fine-tuning, overcomes the guardrail tax
by (1) significantly increasing resistance to iterative jailbreak attacks and
(2) achieving state-of-the-art results in safety guardrailing while (3)
matching helpfulness scores of alignment-tuned models. Extensive evaluations
demonstrate that PrimeGuard, without fine-tuning, outperforms all competing
baselines and overcomes the guardrail tax by improving the fraction of safe
responses from 61% to 97% and increasing average helpfulness scores from 4.17
to 4.29 on the largest models, while reducing attack success rate from 100% to
8%.
  PrimeGuard implementation is available at
https://github.com/dynamofl/PrimeGuard and safe-eval dataset is available at
https://huggingface.co/datasets/dynamoai/safe_eval.
66a0d32919daa28fdecea641
Blazej Manczak
admin_assigned
2024-07-24T12:23:12.545Z
65e5ee0c0c4de934fdc6c01c
/avatars/f5d7c72a51703171e7fade609558cd5a.svg
Blazej Manczak
user
blazejm
66a0d32919daa28fdecea642
Eliott Zemour
claimed_verified
2024-07-24T12:23:35.543Z
6230c0132bba3cd650640529
https://cdn-avatars.huggingface.co/v1/production/uploads/6230c0132bba3cd650640529/YmKBS1JdBTQWL6Qe_kPw5.png
Eliott Zemour
user
eliolio
66a0d32919daa28fdecea643
Eric Lin
66a0d32919daa28fdecea644
Vaikkunth Mugunthan
admin_assigned
2024-07-24T12:24:07.456Z
65a029cfe969415381ca6035
/avatars/fb825981d676dbf41ce74032c87f59cd.svg
Vaikkunth Mugunthan
user
vaikkunth
PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing
Deploying language models (LMs) necessitates outputs to be both high-quality
and compliant with safety guidelines. Although Inference-Time Guardrails (ITG)
offer solutions that shift model output distributions towards compliance, we
find that current methods struggle in balancing safety with helpfulness. ITG
Methods that safely address non-compliant queries exhibit lower helpfulness
while those that prioritize helpfulness compromise on safety. We refer to this
trade-off as the guardrail tax, analogous to the alignment tax. To address
this, we propose PrimeGuard, a novel ITG method that utilizes structured
control flow.
  PrimeGuard routes requests to different self-instantiations of the LM with
varying instructions, leveraging its inherent instruction-following
capabilities and in-context learning. Our tuning-free approach dynamically
compiles system-designer guidelines for each query. We construct and release
safe-eval, a diverse red-team safety benchmark. Extensive evaluations
demonstrate that PrimeGuard, without fine-tuning, overcomes the guardrail tax
by (1) significantly increasing resistance to iterative jailbreak attacks and
(2) achieving state-of-the-art results in safety guardrailing while (3)
matching helpfulness scores of alignment-tuned models. Extensive evaluations
demonstrate that PrimeGuard, without fine-tuning, outperforms all competing
baselines and overcomes the guardrail tax by improving the fraction of safe
responses from 61% to 97% and increasing average helpfulness scores from 4.17
to 4.29 on the largest models, while reducing attack success rate from 100% to
8%.
  PrimeGuard implementation is available at
https://github.com/dynamofl/PrimeGuard and safe-eval dataset is available at
https://huggingface.co/datasets/dynamoai/safe_eval.
PrimeGuard is a novel Inference-Time Guardrails method that enhances both safety and helpfulness by routing queries through self-instantiations of language models with structured control flow.
Inference-Time Guardrails ITG PrimeGuard structured control flow self-instantiations in-context learning system-designer guidelines safe-eval safety benchmark iterative jailbreak attacks helpfulness scores alignment-tuned models