Papers by Varun Dhanraj
Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face challenges in reliably solving reasoning tasks, especially when solving tasks that require strict rule following. |
| Approach: | They propose a method that encodes hidden states into neurosymbolic vectors and decodes them into a neurosample vector space to enable problem-solving within a neural space. |
| Outcome: | The proposed method shows an average of 88.6% lower cross-entropy loss and 15.4 times more problems correctly solved on a suite of mathematical reasoning tasks compared to chain-of-thought prompting and supervised fine-tuning (LoRA). |