Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning
Authors: Subhabrata Dutta, Ishan Pandey, Joykirat Singh, Sunny Manchanda, Soumen Chakrabarti, Tanmoy Chakraborty
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | SYRELM shows massive improvements (e.g., +30.65 absolute point improvement in accuracy on the SVAMP dataset using GPT-J 6B model) over base LMs, while keeping our testbed easy to diagnose, interpret and within reach of most researchers. |
| Researcher Affiliation | Collaboration | 1IIT Delhi, India 2IIIT Delhi, India 3DYSL-AI, India 4IIT Bombay, India |
| Pseudocode | Yes | Figure 2: Example of parsable pseudocode generation from LMs. |
| Open Source Code | Yes | We release the code and data for reproducibility 2. 2https://github.com/joykirat18/SYRELM |
| Open Datasets | Yes | We built upon three existing mathword problem datasets: ASDiv (Miao, Liang, and Su 2020b), MAWPS (Miao, Liang, and Su 2020b), and Math23k (Shen et al. 2021). |
| Dataset Splits | No | The paper mentions 'training data' and 'testbed' but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only mentions 'modest hardware' in a general sense. |
| Software Dependencies | No | The paper mentions using 'Sym Py' as a symbolic solver and that Python code is generated, but it does not provide specific version numbers for Sym Py, Python, or any other relevant software libraries. |
| Experiment Setup | Yes | Details of the hyperparameters used in SYRELM are described in Appendix B. |