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cs.CL2025
Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries
Tianyi Lorena Yan, Robin Jia
To answer one-to-many factual queries (e.g., listing cities of a country), a language model (LM) must simultaneously recall knowledge and avoid repeating previous answers. How are…
cs.CL2024
Monotonic Paraphrasing Improves Generalization of Language Model Prompting
Qin Liu, Fei Wang, Nan Xu +3
Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task. One commonly recognized factor for this phenomenon is the model's…
cs.CL2024
Contrastive Instruction Tuning
Tianyi Lorena Yan, Fei Wang, James Y. Huang +5
Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks. However, current LLMs exhibit limited robustnes…