# LILA: A Unified Benchmark for Mathematical Reasoning

@inproceedings{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}, booktitle={Conference on Empirical Methods in Natural Language Processing}, year={2022} }

Mathematical reasoning skills are essential for general-purpose intelligentsystems to perform tasks from grocery shopping to climate modeling.Towards evaluating and improving AI systems in this domain, we proposeLILA, a unified mathematical reasoning benchmark consisting of 23 diversetasks 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 (iv…

## 12 Citations

### Logical Tasks for Measuring Extrapolation and Rule Comprehension

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### A Survey of Deep Learning for Mathematical Reasoning

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This survey paper reviews the key tasks, datasets, and methods at the intersec-tion of mathematical reasoning and deep learning over the past decade, and evaluates existing benchmarks and methods and discusses future research directions.

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- Computer ScienceArXiv
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### Mathematics, word problems, common sense, and artificial intelligence

- Computer ScienceArXiv
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### Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model

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### Generating Sequences by Learning to Self-Correct

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### UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression

- Computer ScienceEMNLP
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A large-scale Unified Geometry problem benchmark, UniGeo, is constructed and a unified multi-task Geometric Transformer framework, Geoformer, is presented to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation.

### Reasoning with Language Model Prompting: A Survey

- Computer ScienceArXiv
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This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting and introduces research works with comparisons and summaries and provides systematic resources to help beginners.

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

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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…

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