Give me a hint: Can LLMs take a hint to solve math problems?

• URL: http://arxiv.org/abs/2410.05915v2
• Date: Sat, 09 Nov 2024 08:32:47 GMT
• Title: Give me a hint: Can LLMs take a hint to solve math problems?
• Authors: Vansh Agrawal, Pratham Singla, Amitoj Singh Miglani, Shivank Garg, Ayush Mangal,
• Abstract summary: We propose giving "hints" to improve the language model's performance on advanced mathematical problems. We also test robustness to adversarial hints and demonstrate their sensitivity to them.
• Score: 0.5742190785269342
• License:
• Abstract: While state-of-the-art LLMs have shown poor logical and basic mathematical reasoning, recent works try to improve their problem-solving abilities using prompting techniques. We propose giving "hints" to improve the language model's performance on advanced mathematical problems, taking inspiration from how humans approach math pedagogically. We also test robustness to adversarial hints and demonstrate their sensitivity to them. We demonstrate the effectiveness of our approach by evaluating various diverse LLMs, presenting them with a broad set of problems of different difficulties and topics from the MATH dataset and comparing against techniques such as one-shot, few-shot, and chain of thought prompting.

Related papers

• AI-Assisted Generation of Difficult Math Questions [78.7547836422727]
  Current training positions mathematical reasoning as a core capability. There is unmet demand for diverse and challenging math questions. We present a design framework that combines the strengths of LLMs with a human-in-the-loop approach.
  arXiv  Detail & Related papers  (2024-07-30T17:55:36Z)
• MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark [82.64129627675123]
  MathBench is a new benchmark that rigorously assesses the mathematical capabilities of large language models. MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.
  arXiv  Detail & Related papers  (2024-05-20T17:52:29Z)
• 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)
• MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems? [99.0305256706604]
  We introduce MathVerse, an all-around visual math benchmark designed for an equitable and in-depth evaluation of MLLMs. We meticulously collect 2,612 high-quality, multi-subject math problems with diagrams from publicly available sources. This approach allows MathVerse to comprehensively assess whether and how much MLLMs can truly understand the visual diagrams for mathematical reasoning.
  arXiv  Detail & Related papers  (2024-03-21T17:59:50Z)
• FineMath: A Fine-Grained Mathematical Evaluation Benchmark for Chinese Large Language Models [44.63505885248145]
  FineMath is a fine-grained mathematical evaluation benchmark dataset for assessing Chinese Large Language Models (LLMs) FineMath is created to cover the major key mathematical concepts taught in elementary school math, which are divided into 17 categories of math word problems. All the 17 categories of math word problems are manually annotated with their difficulty levels according to the number of reasoning steps required to solve these problems.
  arXiv  Detail & Related papers  (2024-03-12T15:32:39Z)
• GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers [68.77382332826167]
  Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. One essential and frequently occurring evidence is that when the math questions are slightly changed, LLMs can behave incorrectly. This motivates us to evaluate the robustness of LLMs' math reasoning capability by testing a wide range of question variations.
  arXiv  Detail & Related papers  (2024-02-29T15:26:14Z)
• Adversarial Math Word Problem Generation [6.92510069380188]
  We propose a new paradigm for ensuring fair evaluation of large language models (LLMs) We generate adversarial examples which preserve the structure and difficulty of the original questions aimed for assessment, but are unsolvable by LLMs. We conduct experiments on various open- and closed-source LLMs, quantitatively and qualitatively demonstrating that our method significantly degrades their math problem-solving ability.
  arXiv  Detail & Related papers  (2024-02-27T22:07:52Z)
• Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? [140.9751389452011]
  We study the biases of large language models (LLMs) in relation to those known in children when solving arithmetic word problems. We generate a novel set of word problems for each of these tests, using a neuro-symbolic approach that enables fine-grained control over the problem features.
  arXiv  Detail & Related papers  (2024-01-31T18:48:20Z)
• MathAttack: Attacking Large Language Models Towards Math Solving Ability [29.887497854000276]
  We propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems. It is essential to preserve the mathematical logic of original MWPs during the attacking. Extensive experiments on our RobustMath and two another math benchmark GSM8K and MultiAirth datasets show that MathAttack could effectively attack the math solving ability of LLMs.
  arXiv  Detail & Related papers  (2023-09-04T16:02:23Z)
• MathPrompter: Mathematical Reasoning using Large Language Models [7.953723258038284]
  Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks. MathPrompter uses the Zero-shot chain-of-thought prompting technique to generate multiple Algebraic expressions or Python functions to solve the same math problem in different ways.
  arXiv  Detail & Related papers  (2023-03-04T04:43:49Z)

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.