Papers by Yikuan Li
Realistic Training Data Generation and Rule Enhanced Decoding in LLM for NameGuess (2025.emnlp-main)
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| Challenge: | Abbreviated column names often harm downstream tasks, causing performance drops of 10.54, 40.50, and 3.83 percentage points. |
| Approach: | They propose a method that integrates a subsequence abbreviation generator trained on human-annotated data and collects non-subsequent abbrevations to improve the training set. |
| Outcome: | The proposed approach improves on the English NameGuess task and surpasses state-of-the-art LLMs. |
Data-Efficient Automatic Prompt Optimization for Memory-Enhanced Conversational Agents (2025.emnlp-industry)
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| Challenge: | Automatic prompt optimization (APO) uses algorithms to optimize prompts for LLMs . but application to memory-enhanced conversational agents presents unique challenges . |
| Approach: | They propose a framework for automatic prompt optimization for memory-enhanced conversational agents . they leverage LLMs to holistically optimize the prompts of all agents based on memory writing, reading, and response generation . |
| Outcome: | The proposed framework is applied to memory-enhanced conversational agents . it provides a holistic quality score for responses and performs error attribution . |