@inproceedings{raheja-etal-2024-enhancing-large,
title = "Enhancing Large Language Models through Transforming Reasoning Problems into Classification Tasks",
author = "Raheja, Tarun and
Sinha, Raunak and
Deepak, Advit and
Healy, Will and
Srinivasa, Jayanth and
Lee, Myungjin and
Kompella, Ramana",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.532",
pages = "6007--6016",
abstract = "In this paper, we introduce a novel approach for enhancing the reasoning capabilities of large language models (LLMs) for constraint satisfaction problems (CSPs), by converting reasoning problems into classification tasks. Our method leverages the LLM{'}s ability to decide when to call a function from a set of logical-linguistic primitives, each of which can interact with a local {``}scratchpad{''} memory and logical inference engine. Invocation of these primitives in the correct order writes the constraints to the scratchpad memory and enables the logical engine to verifiably solve the problem. We additionally propose a formal framework for exploring the {``}linguistic{''} hardness of CSP reasoning-problems for LLMs. Our experimental results demonstrate that under our proposed method, tasks with significant computational hardness can be converted to a form that is easier for LLMs to solve and yields a 40{\%} improvement over baselines. This opens up new avenues for future research into hybrid cognitive models that integrate symbolic and neural approaches.",
}
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<abstract>In this paper, we introduce a novel approach for enhancing the reasoning capabilities of large language models (LLMs) for constraint satisfaction problems (CSPs), by converting reasoning problems into classification tasks. Our method leverages the LLM’s ability to decide when to call a function from a set of logical-linguistic primitives, each of which can interact with a local “scratchpad” memory and logical inference engine. Invocation of these primitives in the correct order writes the constraints to the scratchpad memory and enables the logical engine to verifiably solve the problem. We additionally propose a formal framework for exploring the “linguistic” hardness of CSP reasoning-problems for LLMs. Our experimental results demonstrate that under our proposed method, tasks with significant computational hardness can be converted to a form that is easier for LLMs to solve and yields a 40% improvement over baselines. This opens up new avenues for future research into hybrid cognitive models that integrate symbolic and neural approaches.</abstract>
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%0 Conference Proceedings
%T Enhancing Large Language Models through Transforming Reasoning Problems into Classification Tasks
%A Raheja, Tarun
%A Sinha, Raunak
%A Deepak, Advit
%A Healy, Will
%A Srinivasa, Jayanth
%A Lee, Myungjin
%A Kompella, Ramana
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F raheja-etal-2024-enhancing-large
%X In this paper, we introduce a novel approach for enhancing the reasoning capabilities of large language models (LLMs) for constraint satisfaction problems (CSPs), by converting reasoning problems into classification tasks. Our method leverages the LLM’s ability to decide when to call a function from a set of logical-linguistic primitives, each of which can interact with a local “scratchpad” memory and logical inference engine. Invocation of these primitives in the correct order writes the constraints to the scratchpad memory and enables the logical engine to verifiably solve the problem. We additionally propose a formal framework for exploring the “linguistic” hardness of CSP reasoning-problems for LLMs. Our experimental results demonstrate that under our proposed method, tasks with significant computational hardness can be converted to a form that is easier for LLMs to solve and yields a 40% improvement over baselines. This opens up new avenues for future research into hybrid cognitive models that integrate symbolic and neural approaches.
%U https://aclanthology.org/2024.lrec-main.532
%P 6007-6016
Markdown (Informal)
[Enhancing Large Language Models through Transforming Reasoning Problems into Classification Tasks](https://aclanthology.org/2024.lrec-main.532) (Raheja et al., LREC-COLING 2024)
ACL
- Tarun Raheja, Raunak Sinha, Advit Deepak, Will Healy, Jayanth Srinivasa, Myungjin Lee, and Ramana Kompella. 2024. Enhancing Large Language Models through Transforming Reasoning Problems into Classification Tasks. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6007–6016, Torino, Italia. ELRA and ICCL.