Training on competing goals improves models’ handling of instruction conflicts

Chen and colleagues report that synthetic text-processing conflicts improve instruction-priority adherence in two models, with little average change on tasks without conflicts.

Big Tech
Sizhe Chen · Sanjay Kariyappa · Liwei Jiang · Chong Xiang · Jihye Choi · Chaowei Xiao · +1 more

NVIDIA · UC Berkeley

Research Digest··2 min read
Illustration: Training on competing goals improves models’ handling of instruction conflicts
Language models should follow user requests only when they do not conflict with higher-priority system instructions.

The authors distinguish goals, which specify what to accomplish, from constraints, which restrict how to accomplish it.

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