Can LLMs Compute with Reasons?
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916131321675776 |
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| author | Sandilya, Harshit Raj, Peehu Bafna, Jainit Sushil Mukhopadhyay, Srija Sharma, Shivansh Sharma, Ellwil Sharma, Arastu Trivedi, Neeta Shrivastava, Manish Kumar, Rajesh |
| author_facet | Sandilya, Harshit Raj, Peehu Bafna, Jainit Sushil Mukhopadhyay, Srija Sharma, Shivansh Sharma, Ellwil Sharma, Arastu Trivedi, Neeta Shrivastava, Manish Kumar, Rajesh |
| contents | Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distributed network of SLMs. This network leverages error-based learning and hint incorporation to refine the reasoning capabilities of SLMs. Our goal is to provide a framework that empowers SLMs to approach the level of logic-based applications achieved by high-parameter models, potentially benefiting any language model. Ultimately, this novel concept paves the way for bridging the logical gap between humans and LLMs across various fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12080 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can LLMs Compute with Reasons? Sandilya, Harshit Raj, Peehu Bafna, Jainit Sushil Mukhopadhyay, Srija Sharma, Shivansh Sharma, Ellwil Sharma, Arastu Trivedi, Neeta Shrivastava, Manish Kumar, Rajesh Computation and Language 68T50 I.2.7 Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distributed network of SLMs. This network leverages error-based learning and hint incorporation to refine the reasoning capabilities of SLMs. Our goal is to provide a framework that empowers SLMs to approach the level of logic-based applications achieved by high-parameter models, potentially benefiting any language model. Ultimately, this novel concept paves the way for bridging the logical gap between humans and LLMs across various fields. |
| title | Can LLMs Compute with Reasons? |
| topic | Computation and Language 68T50 I.2.7 |
| url | https://arxiv.org/abs/2402.12080 |