Papers with EPI
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index (2025.coling-main)
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| Challenge: | Prompt engineering is a growing subdiscipline of natural language processing . a lack of appropriate consideration for the financial constraints of computationally burdensome methods can limit their adoption and impact. |
| Approach: | They propose a new metric that combines accuracy scores with token consumption to reflect different resource constraints. |
| Outcome: | The economic prompting index (EPI) measures the performance of 6 prompting techniques across 10 widely-used language models and 4 diverse datasets. |
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)
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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng
| Challenge: | Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting. |
| Approach: | They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance. |
| Outcome: | The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity. |