@inproceedings{dhanraj-eliasmith-2025-improving,
title = "Improving Rule-based Reasoning in {LLM}s using Neurosymbolic Representations",
author = "Dhanraj, Varun and
Eliasmith, Chris",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1556/",
doi = "10.18653/v1/2025.emnlp-main.1556",
pages = "30577--30596",
ISBN = "979-8-89176-332-6",
abstract = "Large language models (LLMs) continue to face challenges in reliably solving reasoning tasks, particularly tasks that involve precise rule following, as often found in mathematical reasoning tasks. This paper introduces a novel neurosymbolic method that improves LLM reasoning by encoding hidden states into neurosymbolic vectors, enabling problem-solving within a neurosymbolic vector space. The results are decoded and merged with the original hidden state, significantly boosting the model{'}s performance on numerical reasoning tasks. By offloading computation through neurosymbolic representations, this method enhances efficiency, reliability, and interpretability. Our experimental results demonstrate 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), while not hindering the LLM{'}s performance on other tasks. We make our code available at https://github.com/vdhanraj/Neurosymbolic-LLM."
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<abstract>Large language models (LLMs) continue to face challenges in reliably solving reasoning tasks, particularly tasks that involve precise rule following, as often found in mathematical reasoning tasks. This paper introduces a novel neurosymbolic method that improves LLM reasoning by encoding hidden states into neurosymbolic vectors, enabling problem-solving within a neurosymbolic vector space. The results are decoded and merged with the original hidden state, significantly boosting the model’s performance on numerical reasoning tasks. By offloading computation through neurosymbolic representations, this method enhances efficiency, reliability, and interpretability. Our experimental results demonstrate 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), while not hindering the LLM’s performance on other tasks. We make our code available at https://github.com/vdhanraj/Neurosymbolic-LLM.</abstract>
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%0 Conference Proceedings
%T Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations
%A Dhanraj, Varun
%A Eliasmith, Chris
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F dhanraj-eliasmith-2025-improving
%X Large language models (LLMs) continue to face challenges in reliably solving reasoning tasks, particularly tasks that involve precise rule following, as often found in mathematical reasoning tasks. This paper introduces a novel neurosymbolic method that improves LLM reasoning by encoding hidden states into neurosymbolic vectors, enabling problem-solving within a neurosymbolic vector space. The results are decoded and merged with the original hidden state, significantly boosting the model’s performance on numerical reasoning tasks. By offloading computation through neurosymbolic representations, this method enhances efficiency, reliability, and interpretability. Our experimental results demonstrate 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), while not hindering the LLM’s performance on other tasks. We make our code available at https://github.com/vdhanraj/Neurosymbolic-LLM.
%R 10.18653/v1/2025.emnlp-main.1556
%U https://aclanthology.org/2025.emnlp-main.1556/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1556
%P 30577-30596
Markdown (Informal)
[Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations](https://aclanthology.org/2025.emnlp-main.1556/) (Dhanraj & Eliasmith, EMNLP 2025)
ACL