Papers by Dujian Ding
LLM Performance Predictors are good initializers for Architecture Search (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have diverse applications, encompassing both open-ended tasks (e.g., brainstorming and chat) and closed-ended ones (eg. question answering). |
| Approach: | They construct PP prompts for Large Language Models (LLMs) that estimate the performance of specific deep neural network architectures on downstream tasks. |
| Outcome: | The proposed model achieves a SoTA mean absolute error and a slight degradation in rank correlation coefficient compared to baseline predictors in machine translation tasks. |
Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs (2025.acl-long)
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| Challenge: | Chain-of-Thought (CoT) prompting has emerged as a practical workaround, but most CoT-based methods rely on a single generic prompt like “think step by step” with no task-specific adaptation. |
| Approach: | They propose a theoretical framework that explains why some prompts succeed while others fail by using a generic generic prompt like "think step by step" they show that prompts function as selectors, extracting specific task-relevant information from the model's full hidden state during CoT reasoning. |
| Outcome: | The proposed framework explains why some prompts succeed while others fail. |