Prism-Δ: Differential Subspace Steering for Prompt Highlighting in Large Language Models
Prompt highlighting steers a large language model to prioritize user-specified text spans during generation. A key challenge is extracting steering directions that capture the difference between relevant and irrelevant contexts, rather than shared structural patterns common to both. We propose PRISM-Δ (Projection-based Relevance-Informed Steering Method), which decomposes the difference between positive and negative cross-covariance matrices to maximize discriminative energy while eliminating shared directions. Each attention head receives a continuous softplus importance weight, letting weak-but-useful heads contribute at reduced strength. The framework extends naturally to Value representations, capturing content-channel signal that Key-only methods leave unused. Across four benchmarks and five models, PRISM-Δ matches or exceeds the best existing method on 19 of 20 configurations, with relative gains up to +10.6%, while halving the fluency cost of steering. PRISM-Δ also scales to long-context retrieval, outperforming the best existing method by up to +4.8% relative gain. PRISM-Δ is compatible with FlashAttention and adds negligible memory overhead.
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Yuyao Ge
admin_assigned
2026-03-12T09:33:01.366Z
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YuyaoGe
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YuyaoGe
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Shenghua Liu
admin_assigned
2026-03-12T09:33:07.454Z
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Shenghua Liu
shenghua-liu
user
shenghua-liu
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Yiwei Wang
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Tianyu Liu
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Baolong Bi
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2026-03-12T09:33:25.355Z
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Bibaolong
Bibaolong
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Bibaolong
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Lingrui Mei
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2026-03-12T09:33:31.253Z
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Lingrui Mei
Chevalier
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Chevalier
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Jiayu Yao
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Jiafeng Guo
admin_assigned
2026-03-12T09:33:45.293Z
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Jiafeng Guo
JiafengGuo
user
JiafengGuo
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Xueqi Cheng
admin_assigned
2026-03-12T09:33:50.724Z
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Xueqi Cheng
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xueqic
Prism-Δ: Differential Subspace Steering for Prompt Highlighting in Large Language Models
Prompt highlighting steers a large language model to prioritize user-specified text spans during generation. A key challenge is extracting steering directions that capture the difference between relevant and irrelevant contexts, rather than shared structural patterns common to both. We propose PRISM-Δ (Projection-based Relevance-Informed Steering Method), which decomposes the difference between positive and negative cross-covariance matrices to maximize discriminative energy while eliminating shared directions. Each attention head receives a continuous softplus importance weight, letting weak-but-useful heads contribute at reduced strength. The framework extends naturally to Value representations, capturing content-channel signal that Key-only methods leave unused. Across four benchmarks and five models, PRISM-Δ matches or exceeds the best existing method on 19 of 20 configurations, with relative gains up to +10.6%, while halving the fluency cost of steering. PRISM-Δ also scales to long-context retrieval, outperforming the best existing method by up to +4.8% relative gain. PRISM-Δ is compatible with FlashAttention and adds negligible memory overhead.
PRISM-Δ extracts discriminative steering directions by decomposing cross-covariance differences, uses softplus weights for attention heads, and extends to value representations for improved long-context retrieval performance.
prompt highlighting large language model steering directions cross-covariance matrices discriminative energy attention heads softplus importance weight value representations FlashAttention long-context retrieval