Effective Graph-of-Thought Reasoning in Language Models

Основні поняття

Graph-of-Thought (GoT) reasoning enhances language models by capturing the non-sequential nature of human thinking through thought graphs.

Анотація

The article introduces GoT reasoning as a novel approach to modeling human thought processes.
It proposes a two-stage framework for GoT, incorporating thought graph representation and fusion mechanisms.
GoT outperforms traditional CoT prompting methods on text-only and multimodal reasoning tasks.
Ablation studies demonstrate the importance of structured thought graphs in enhancing LM performance.
Performance analysis shows significant improvements over existing models across different subjects and question classes.

Статистика

Our model achieves an accuracy of 87.59% on ScienceQA test set using the T5-base model.
The FLAN-Alpacalarge model achieves an accuracy of 33.73% on AQUA-RAT test set.

Цитати

"If you want it, you GoT it!"

Ключові висновки, отримані з

Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models

by Yao Yao,Zuch... о arxiv.org 03-26-2024

https://arxiv.org/pdf/2305.16582.pdf

Глибші Запити

How does the incorporation of thought graphs impact the interpretability of language models?

Incorporating thought graphs in language models (LMs) significantly enhances their interpretability by providing a structured framework for reasoning. Thought graphs model human thinking processes as not just linear chains but also as interconnected nodes and edges, capturing the non-sequential nature of cognition. By representing thoughts as nodes and connections between them as edges, LM's reasoning becomes more transparent and logical. This approach allows LMs to understand relationships between different entities, make deductions based on evidence, and generate more accurate answers. The visual representation of these interconnected thoughts aids in understanding how decisions are made within the model.

What are the potential drawbacks or limitations of utilizing Graph-of-Thought reasoning in LM tasks?

While Graph-of-Thought (GoT) reasoning offers significant benefits, there are some potential drawbacks and limitations to consider:

Computational Complexity: Incorporating thought graphs may increase computational complexity and training times due to the additional processing required for graph construction.
Data Dependency: Effective utilization of GoT relies on high-quality data with clear relationships between entities; noisy or ambiguous data can lead to inaccurate modeling.
Interpretation Challenges: Understanding complex graph structures generated by GoT may pose challenges for users trying to interpret how decisions are made within the model.
Scalability Issues: Scaling up GoT for larger datasets or more complex tasks may require substantial resources and optimization efforts.

How might Graph-of-Thought reasoning be applied to other domains beyond NLP research?

Graph-of-Thought (GoT) reasoning has applications beyond NLP research in various domains where complex decision-making processes occur:

Medical Diagnosis: In healthcare, GoT could help doctors analyze patient symptoms, test results, medical history, etc., forming a comprehensive diagnostic graph for accurate disease identification.
Financial Analysis: For financial institutions, GoT can assist in risk assessment by connecting market trends, economic indicators, company performance metrics into a coherent graph structure.
Scientific Research: In scientific fields like biology or chemistry, researchers could use GoTs to map out experimental procedures, hypotheses testing steps leading to novel discoveries.
Engineering Design: Engineers can utilize GoTs for product development cycles by linking design requirements with engineering constraints through an interconnected graph representation.

By applying Graph-of-Thought reasoning outside NLP contexts across diverse disciplines that involve intricate decision-making processes will enhance problem-solving capabilities and improve overall outcomes through structured analysis and interpretation methods similar to those used in natural language processing tasks using LMs incorporating this technique into their workflows