Papers with preserves accuracy
Dynamic Retriever for In-Context Knowledge Editing via Policy Optimization (2025.emnlp-main)
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| Challenge: | Large language models excel at factual recall yet propagate stale or incorrect knowledge. |
| Approach: | They propose a framework that allows users to rank demonstrations by editing reward . it uses a *learnable threshold* to prune low-value examples, reducing edit success by 17.1% . |
| Outcome: | The proposed framework improves edit success by 17.1% and reduces latency by 41.6% on the CounterFact benchmark. |
Profiling-Free Mixed-Precision Quantization for MoE LLMs via Fuzzy Rule Interpolation (2026.acl-long)
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| Challenge: | Large Language Models are scaling in size and capability, driving substantial computational and memory costs. |
| Approach: | They propose a mixed-precision quantization framework that uses fuzzy rule interpolation to predict quantization error from only sparse samples. |
| Outcome: | The proposed framework accelerates the profiling phase by up to 15.7 on DeepSeek-V2 while achieving comparable or slightly superior zero-shot accuracy. |
Quantize What Counts: More for Keys, Less for Values (2026.findings-acl)
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| Challenge: | Empirical evaluations across various prominent LLMs and benchmarks show that key-favored allocations retain up to 98.3% accuracy compared to uniform allocations (e.g., 4-bit keys, 2-bit values). |
| Approach: | They propose two theorems that anchor mixed-precision KV quantization in the intrinsic geometry of Transformer models. |
| Outcome: | Empirical evaluations show that key-favored allocations retain up to 98.3% accuracy while conserving memory. |