Papers by Yide Liu
SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) demonstrate remarkable performance across diverse tasks, yet their effectiveness often depends on costly commercial APIs or cloud services. |
| Approach: | They propose a dual-mode compatible approach that fine-tunes models through shortest-response preference optimization and a confidence-aware rejection mechanism. |
| Outcome: | The proposed approach reduces redundant outputs and response times while reducing computational costs by over 50% and cascade latency by over 80%. |