Papers by Lionel Ni
Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs (2026.findings-eacl)
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| Challenge: | Existing methods to protect PII from training on small corpora are difficult to implement in real-world applications. |
| Approach: | They propose an entity-based framework that synthesizes encrypted training data to protect PII. |
| Outcome: | The proposed framework outperforms base models and ensures PII security on limited-scale datasets while exhibiting a modest performance gap compared to models trained on unencrypted synthetic data. |
Conflicts Make Large Reasoning Models Vulnerable to Attacks (2026.findings-acl)
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| Challenge: | Large Reasoning Models have demonstrated outstanding capabilities in solving complex reasoning tasks by incorporating step-by-step chain-of-thought (CoT) reasoning. |
| Approach: | They evaluate three large reasoning models that perform explicit and coherent reasoning under conflicting objectives and use them to evaluate their performance. |
| Outcome: | The proposed models perform explicit and coherent reasoning before producing their outputs, improving problem-solving and multi-step decision making. |
Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment (2025.emnlp-demos)
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| Challenge: | Traditional methods of alpha mining have inherent limitations, especially in implementing the ideas of quant researchers. |
| Approach: | They propose a new alpha mining paradigm by introducing human-AI interaction and a prompt engineering algorithmic framework to implement this paradigm by using large language models. |
| Outcome: | The proposed framework is based on human-AI interaction and large language models and is comparable to human participants in the WorldQuant International Quant Championship. |