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cs.CL2024
Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation
Chenyang An, Shima Imani, Feng Yao +8
In the field of large language model (LLM)-based proof generation, despite extensive training on large datasets such as ArXiv, LLMs still exhibit only modest performance on proving…
cs.CL2024
Learning How To Ask: Cycle-Consistency Refines Prompts in Multimodal Foundation Models
Maurice Diesendruck, Jianzhe Lin, Shima Imani +3
When LLMs perform zero-shot inference, they typically use a prompt with a task specification, and generate a completion. However, there is no work to explore the possibility of the…
cs.CL2024
Exploring Group and Symmetry Principles in Large Language Models
Shima Imani, Hamid Palangi
Large Language Models (LLMs) have demonstrated impressive performance across a wide range of applications; however, assessing their reasoning capabilities remains a significant cha…