# Lila: A Unified Benchmark for Mathematical Reasoning

@article{Mishra2022LilaAU, title={Lila: A Unified Benchmark for Mathematical Reasoning}, author={Swaroop Mishra and Matthew Finlayson and Pan Lu and Leonard Tang and Sean Welleck and Chitta Baral and Tanmay Rajpurohit and Oyvind Tafjord and Ashish Sabharwal and Peter Clark and A. Kalyan}, journal={ArXiv}, year={2022}, volume={abs/2210.17517} }

Mathematical reasoning skills are essential for general-purpose intelli-gent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we propose L¯ila , a uniﬁed mathematical reasoning benchmark consisting of 23 diverse tasks along four dimensions: (i) mathematical abilities e.g., arithmetic, calculus (ii) language format e.g., question-answering, ﬁll-in-the-blanks (iii) language diversity e.g., no language, simple language…

## 5 Citations

### Logical Tasks for Measuring Extrapolation and Rule Comprehension

- Computer Science, PhilosophyArXiv
- 2022

This work describes and characterize logical tasks and discusses system requirements for their solution, and discusses the relevance of logical tasks to concepts such as extrapolation, explainability, and inductive bias.

### Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

- Computer ScienceArXiv
- 2022

This work designs language models to learn to generate lectures and explanations as the chain of thought (CoT) to mimic the multi-hop reasoning process when answering S CIENCE QA questions and explores the upper bound of GPT-3 and shows that CoT helps language models learn from fewer data.

### Generating Sequences by Learning to Self-Correct

- Computer ScienceArXiv
- 2022

SELF - CORRECTION is presented, an approach that decouples an imperfect base generator from a separate corrector that learns to iteratively correct imperfect generations and improves upon the base generator in three diverse generation tasks– mathematical program synthesis, lexically-constrained generation, and toxicity control.

### G ENERATING S EQUENCES BY L EARNING TO [S ELF -]C ORRECT

- Computer Science
- 2022

Self-correction provides a flexible framework for improving the performance of off-the-shelf and fine-tuned language models on a wide range of tasks by decomposing generation into a base generator…

### Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

- Computer Science
- 2022

Under both few-shot and zero-shot settings, PoT can show an average performance gain over CoT by around 12% across all the evaluated datasets, and by combining PoT with self-consistency decoding, can achieve SoT performance on all math problem datasets and near-SoTA performance on ﬁnancial datasets.

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