Puzzle Solving using Reasoning of Large Language Models: A Survey
- URL: http://arxiv.org/abs/2402.11291v3
- Date: Sat, 14 Sep 2024 06:12:36 GMT
- Title: Puzzle Solving using Reasoning of Large Language Models: A Survey
- Authors: Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou,
- Abstract summary: This survey examines the capabilities of Large Language Models (LLMs) in puzzle solving.
Our findings highlight the disparity between LLM capabilities and human-like reasoning.
The survey underscores the necessity for novel strategies and richer datasets to advance LLMs' puzzle-solving proficiency.
- Score: 1.9939549451457024
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Exploring the capabilities of Large Language Models (LLMs) in puzzle solving unveils critical insights into their potential and challenges in AI, marking a significant step towards understanding their applicability in complex reasoning tasks. This survey leverages a unique taxonomy -- dividing puzzles into rule-based and rule-less categories -- to critically assess LLMs through various methodologies, including prompting techniques, neuro-symbolic approaches, and fine-tuning. Through a critical review of relevant datasets and benchmarks, we assess LLMs' performance, identifying significant challenges in complex puzzle scenarios. Our findings highlight the disparity between LLM capabilities and human-like reasoning, particularly in those requiring advanced logical inference. The survey underscores the necessity for novel strategies and richer datasets to advance LLMs' puzzle-solving proficiency and contribute to AI's logical reasoning and creative problem-solving advancements.
Related papers
- Advancing Reasoning in Large Language Models: Promising Methods and Approaches [0.0]
Large Language Models (LLMs) have succeeded remarkably in various natural language processing (NLP) tasks.
Their ability to perform complex reasoning-spanning logical deduction, mathematical problem-solving, commonsense inference, and multi-step reasoning-often falls short of human expectations.
This survey provides a comprehensive review of emerging techniques enhancing reasoning in LLMs.
arXiv Detail & Related papers (2025-02-05T23:31:39Z) - ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning [92.76959707441954]
We introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM reasoning performance.
ZebraLogic enables the generation of puzzles with controllable and quantifiable complexity.
Our results reveal a significant decline in accuracy as problem complexity grows.
arXiv Detail & Related papers (2025-02-03T06:44:49Z) - Probing Large Language Models in Reasoning and Translating Complex Linguistic Puzzles [0.6144680854063939]
This paper investigates the utilization of Large Language Models (LLMs) for solving complex linguistic puzzles.
Using datasets from the Puzzling Machine Competition and various Linguistics Olympiads, we employ a comprehensive set of metrics to assess the performance of GPT-4 0603.
arXiv Detail & Related papers (2025-02-02T14:53:14Z) - BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts [59.83547898874152]
We introduce BloomWise, a new prompting technique, inspired by Bloom's taxonomy, to improve the performance of Large Language Models (LLMs)
The decision regarding the need to employ more sophisticated cognitive skills is based on self-evaluation performed by the LLM.
In extensive experiments across 4 popular math reasoning datasets, we have demonstrated the effectiveness of our proposed approach.
arXiv Detail & Related papers (2024-10-05T09:27:52Z) - Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making.
Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations.
Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
arXiv Detail & Related papers (2024-08-17T11:49:53Z) - Multi-step Inference over Unstructured Data [2.169874047093392]
High-stakes decision-making tasks in fields such as medical, legal and finance require a level of precision, comprehensiveness, and logical consistency.
We have developed a neuro-symbolic AI platform to tackle these problems.
The platform integrates fine-tuned LLMs for knowledge extraction and alignment with a robust symbolic reasoning engine.
arXiv Detail & Related papers (2024-06-26T00:00:45Z) - MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.
We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.
Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
arXiv Detail & Related papers (2024-06-20T03:50:23Z) - Optimizing Language Model's Reasoning Abilities with Weak Supervision [48.60598455782159]
We present textscPuzzleBen, a weakly supervised benchmark that comprises 25,147 complex questions, answers, and human-generated rationales.
A unique aspect of our dataset is the inclusion of 10,000 unannotated questions, enabling us to explore utilizing fewer supersized data to boost LLMs' inference capabilities.
arXiv Detail & Related papers (2024-05-07T07:39:15Z) - Improving Large Language Models in Event Relation Logical Prediction [33.88499005859982]
Event relation extraction is a challenging task that demands thorough semantic understanding and rigorous logical reasoning.
In this paper, we conduct an in-depth investigation to systematically explore the capability of LLMs in understanding and applying event relation logic.
Our study reveals that LLMs are not logically consistent reasoners, which results in their suboptimal performance on tasks that need rigorous reasoning.
arXiv Detail & Related papers (2023-10-13T14:53:06Z) - Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems.
LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning.
We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
arXiv Detail & Related papers (2023-10-02T01:00:50Z)
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.