Papers with symbolic engine
Multi-Operational Mathematical Derivations in Latent Space (2024.naacl-long)
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| Challenge: | Using a symbolic engine, we investigate the possibility of approximating multiple mathematical operations in latent space for expression derivation. |
| Approach: | They propose to model mathematical operations as explicit geometric transformations by leveraging a symbolic engine and a large-scale dataset. |
| Outcome: | The proposed paradigms can be used to approximate multiple mathematical operations in latent space, while discriminating the conclusions for a single operation is achievable in the original expression encoder. |
A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers (2024.naacl-long)
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| Challenge: | evaluating the generalisability of Transformers to out-of-distribution mathematical reasoning problems is a challenge for many open-source models. |
| Approach: | They propose a method for generating and perturbing detailed derivations of equations at scale, aided by a symbolic engine, and compare their results to sequence classification tasks. |
| Outcome: | The proposed framework outperforms GPT-4, GPT-3.5 and a canon of fine-tuned BERT models in classification tasks . perturbations to input reasoning can reduce their performance by up to 80 F1 points . |
Hy-NLI: a Hybrid system for Natural Language Inference (2020.coling-main)
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| Challenge: | Recent work has revealed the generalization difficulties of deep models . however, such models fail to perform well on adversarial datasets with challenging linguistic phenomena. |
| Approach: | They propose a hybrid system that learns to identify an NLI pair as linguistically challenging or not . their system uses symbolic or deep learning components to make the final inference decision . |
| Outcome: | The proposed system outperforms existing models on adversarial datasets and on mainstream datasets. |