An Automatic Prompt Generation System for Tabular Data Tasks
Akella Ashlesha, Abhijit Manatkar, et al.
NAACL 2024
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models. We find that subtle alterations in the surface form can significantly impact the answer distribution and the solve rate, exposing the language model’s lack of robustness and sensitivity to the surface form in reasoning through complex problems. To improve mathematical reasoning performance, we propose Self-Consistency-over-Paraphrases (SCoP), which diversifies reasoning paths from specific surface forms of the problem. We evaluate our approach on four mathematics reasoning benchmarks over three large language models and show that SCoP improves mathematical reasoning performance over vanilla self-consistency, particularly for problems initially deemed unsolvable. Finally, we provide additional experiments and discussion regarding problem difficulty and surface forms, including cross-model difficulty agreement and paraphrasing transferability, and Variance of Variations (VOV) for language model evaluation.
Akella Ashlesha, Abhijit Manatkar, et al.
NAACL 2024
Amadou Ba
Big Data 2024
Svetoslav Nizhnichenkov, Rahul Nair, et al.
NeurIPS 2023
Lior Ness, Ella Barkan, et al.
MICCAI 2020