Logic Contrastive Reasoning with Lightweight Large Language Model for Math Word Problems

• URL: http://arxiv.org/abs/2409.00131v1
• Date: Thu, 29 Aug 2024 08:26:42 GMT
• Title: Logic Contrastive Reasoning with Lightweight Large Language Model for Math Word Problems
• Authors: Ding Kai, Ma Zhenguo, Yan Xiaoran,
• Abstract summary: This study focuses on improving the performance of lightweight Large Language Models (LLMs) in mathematical reasoning tasks. We introduce a novel method for measuring mathematical logic similarity and design an automatic screening mechanism. By employing carefully crafted positive and negative example prompts, we guide the model towards adopting sound reasoning logic.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study focuses on improving the performance of lightweight Large Language Models (LLMs) in mathematical reasoning tasks. We introduce a novel method for measuring mathematical logic similarity and design an automatic screening mechanism to construct a set of reference problems that integrate both semantic and logical similarity. By employing carefully crafted positive and negative example prompts, we guide the model towards adopting sound reasoning logic. To the best of our knowledge, this is the first attempt to utilize retrieval-enhanced generation for mathematical problem-solving. Experimental results demonstrate that our method achieves a 15.8% improvement over the Chain of Thought approach on the SVAMP dataset and a 21.5 % improvement on the GSM8K dataset. Further application of this method to a large-scale model with 175 billion parameters yields performance comparable to the best results on both aforementioned datasets. Finally, we conduct an analysis of errors during the reasoning process, providing valuable insights and directions for future research on reasoning tasks using large language models.

Related papers

• Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models [0.0]
  Large models pre-trained on high-quality data exhibit excellent performance across various reasoning tasks. Smaller student models learn from teacher models, and data augmentation, such as rephrasing questions. Despite these efforts, smaller models struggle with arithmetic computations, leading to errors in mathematical reasoning.
  arXiv  Detail & Related papers  (2025-02-18T13:43:06Z)
• Examining False Positives under Inference Scaling for Mathematical Reasoning [59.19191774050967]
  This paper systematically examines the prevalence of false positive solutions in mathematical problem solving for language models. We explore how false positives influence the inference time scaling behavior of language models.
  arXiv  Detail & Related papers  (2025-02-10T07:49:35Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model. We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs [76.43407125275202]
  o1-like models can emulate human-like long-time thinking during inference. This paper presents the first comprehensive study on the prevalent issue of overthinking in these models. We propose strategies to mitigate overthinking, streamlining reasoning processes without compromising accuracy.
  arXiv  Detail & Related papers  (2024-12-30T18:55:12Z)
• Benchmarking Large Language Models for Math Reasoning Tasks [12.91916443702145]
  We compare seven state-of-the-art in-context learning algorithms for mathematical problem solving across five widely used mathematical datasets on four powerful foundation models. Our results indicate that larger foundation models like GPT-4o and LLaMA 3-70B can solve mathematical reasoning independently from the concrete prompting strategy. We open-source our benchmark code to support the integration of additional models in future research.
  arXiv  Detail & Related papers  (2024-08-20T13:34:17Z)
• MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time [51.5039731721706]
  MindStar is a purely inference-based searching method for large language models. It formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. It significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1.
  arXiv  Detail & Related papers  (2024-05-25T15:07:33Z)
• DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models [18.96271708412086]
  Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. We introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer.
  arXiv  Detail & Related papers  (2023-10-08T08:52:13Z)
• Large Language Models as Analogical Reasoners [155.9617224350088]
  Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks. We introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models.
  arXiv  Detail & Related papers  (2023-10-03T00:57:26Z)
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher [83.98181046650664]
  We present an analysis of Transformer-based language model performance across a wide range of model scales. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language. We discuss the application of language models to AI safety and the mitigation of downstream harms.
  arXiv  Detail & Related papers  (2021-12-08T19:41:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.