transformers machine-learning attention-mechanisms ai-research

Do transformers need three projections? Systematic study of QKV variants

arxiv.org

June 4, 2026

2 min read

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59/100

Summary

Transformers utilize a query, key, and value (QKV) attention formulation that is crucial for AI tasks. The study investigates the individual contributions of these three projections and the effects of omitting any of them.

Key Takeaways

The study evaluates three projection sharing constraints in transformers: shared key-value (Q-K=V), shared query-key (Q=K-V), and single projection (Q=K=V).
The Q-K=V projection sharing achieves a 50% reduction in key-value cache with only a 3.1% increase in perplexity during language modeling tasks.
Combining Q-K=V with group query attention (GQA) or multi-query attention (MQA) can yield cache reductions of 87.5% and 96.9%, respectively, facilitating practical on-device inference.
The research systematically characterizes projection sharing as a form of weight tying in attention, providing quantifiable memory benefits for edge deployment.

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Coverage & discussions

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Analysis ready

Community Sentiment

Mixed

Positives

The exploration of QKV variants through ablation studies is valuable, as it can lead to insights on model simplifications that may benefit hardware-constrained environments.
The discussion around the correlation between performance and sequence lengths for the Q-K=V model highlights important considerations for future research in transformer architectures.

Concerns

The limited training data of 10B tokens for a 1.2B model raises concerns about the generalizability of the findings, especially when compared to modern models trained on significantly larger datasets.
Confusing notation in the paper detracts from the clarity of the research, potentially hindering understanding and application of the proposed concepts.