Cumulative Reasoning with Large Language Models

• URL: http://arxiv.org/abs/2308.04371v6
• Date: Tue, 2 Apr 2024 03:37:39 GMT
• Title: Cumulative Reasoning with Large Language Models
• Authors: Yifan Zhang, Jingqin Yang, Yang Yuan, Andrew Chi-Chih Yao,
• Abstract summary: Cumulative Reasoning (CR) is a novel approach that utilizes language models cumulatively and iteratively. We demonstrate CR's superiority through several complex reasoning tasks. CR sets new state-of-the-art on the MATH dataset.
• Score: 12.267474250936123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the recent advancements in language models (LMs), their ability to solve complex problems remains limited. This paper introduces Cumulative Reasoning (CR), a novel approach that utilizes LMs cumulatively and iteratively, mirroring human thought processes for problem-solving. CR decomposes tasks into smaller, manageable components and leverages previous propositions for effective composition, significantly enhancing problem-solving capabilities. We demonstrate CR's superiority through several complex reasoning tasks: it outperforms existing methods in logical inference tasks with up to a 9.3% improvement, achieving 98.04% accuracy on the curated FOLIO wiki dataset. In the Game of 24, it achieves 98% accuracy, marking a 24% improvement over the prior state-of-the-art. Additionally, CR sets new state-of-the-art on the MATH dataset, achieving a 4.2% increase from previous methods and a 43% relative improvement in the most challenging problems. By extending CR to incorporate a code environment without external aids like retrieval or web browsing, we further harness the computational and logical reasoning capabilities of LMs, achieving a remarkable 72.2% accuracy on the MATH dataset and outperforming the PAL/PoT method by 38.8%. Our work not only sets new state-of-the-art but also paves the way toward more sophisticated AI reasoning methods. The code is available at https://github.com/iiis-ai/cumulative-reasoning.

Related papers

• Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning [7.702162381335683]
  Step-by-step Chain-of-Thought (CoT) inference has advanced the mathematical capabilities of large language models (LLMs) We propose a novel method called Step Guidance Reasoning without involving further model fine-tuning. Our method significantly improved the math performance, raising the accuracy on the AMC23 dataset from 30% to 57.5%, a relative improvement of 91.7%, and on the sampled level 5 problem of the MATH dataset, we achieved a relative accuracy improvement of 55.8%, increasing from 43% to 67%.
  arXiv  Detail & Related papers  (2024-10-18T01:38:24Z)
• Instructive Code Retriever: Learn from Large Language Model's Feedback for Code Intelligence Tasks [10.867880635762395]
  We introduce a novel approach named Instructive Code Retriever (ICR) ICR is designed to retrieve examples that enhance model inference across various code intelligence tasks and datasets. We evaluate our model's effectiveness on various tasks, i.e., code summarization, program synthesis, and bug fixing.
  arXiv  Detail & Related papers  (2024-10-15T05:44:00Z)
• BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search [22.672130194493793]
  Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains. They still encounter difficulties in solving mathematical problems due to the rigorous and logical nature of mathematics. We propose a novel approach, BEATS, to enhance mathematical problem-solving abilities.
  arXiv  Detail & Related papers  (2024-09-26T15:47:42Z)
• Building Math Agents with Multi-Turn Iterative Preference Learning [56.71330214021884]
  This paper studies the complementary direct preference learning approach to further improve model performance. Existing direct preference learning algorithms are originally designed for the single-turn chat task. We introduce a multi-turn direct preference learning framework, tailored for this context.
  arXiv  Detail & Related papers  (2024-09-04T02:41:04Z)
• 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)
• Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems [50.76385564061713]
  Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. CoT usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. We propose Deeply Understanding the Problems (DUP) to improve the LLMs' math problem-solving ability by addressing semantic misunderstanding errors.
  arXiv  Detail & Related papers  (2024-04-23T12:16:05Z)
• Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models [102.72940700598055]
  In reasoning tasks, even a minor error can cascade into inaccurate results. We develop a method that avoids introducing external resources, relying instead on perturbations to the input. Our training approach randomly masks certain tokens within the chain of thought, a technique we found to be particularly effective for reasoning tasks.
  arXiv  Detail & Related papers  (2024-03-04T16:21:54Z)
• The Chronicles of RAG: The Retriever, the Chunk and the Generator [0.0]
  This paper presents good practices to implement, optimize, and evaluate RAG for the Brazilian Portuguese language. We explore a diverse set of methods to answer questions about the first Harry Potter book.
  arXiv  Detail & Related papers  (2024-01-15T18:25:18Z)
• Tool-Augmented Reward Modeling [58.381678612409]
  We propose a tool-augmented preference modeling approach, named Themis, to address limitations by empowering RMs with access to external environments. Our study delves into the integration of external tools into RMs, enabling them to interact with diverse external sources. In human evaluations, RLHF trained with Themis attains an average win rate of 32% when compared to baselines.
  arXiv  Detail & Related papers  (2023-10-02T09:47:40Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)

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.