16 citations · 19 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
Shima Imani, Ali Beyram, Harsh Shrivastava
In this paper, we introduce DiversiGATE, a unified framework that consolidates diverse methodologies for LLM verification. The proposed framework comprises two main components: Div…
MathPrompter: Mathematical Reasoning using Large Language Models
Shima Imani, Liang Du, Harsh Shrivastava
Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math probl…