Integrating Natural Language and Tools: Tackling Complex Mathematical Challenges with TORA

Introducing TORA: Blending Natural Language Reasoning with External Tools for Mathematical Problem-Solving

Artificial intelligence and mathematical problem-solving have seen significant advancements with the emergence of large language models. However, these models still struggle with complex mathematical challenges. To address this, researchers from Microsoft and Tsinghua University have developed TORA (Tool-integrated Reasoning Agents), a groundbreaking approach that combines natural language reasoning with external computational tools.

The Challenge of Complex Mathematical Problems

While language models have made progress in mathematical reasoning, complex mathematics remains a difficult task. To improve mathematical prowess, researchers have explored step-by-step natural language reasoning and program synthesis. By integrating external tools like calculators and equation solvers, TORA outperforms open-source models on mathematical reasoning datasets, achieving high accuracy, particularly on the MATHS dataset.

Training TORA Models and Achievements

TORA models were trained using interactive tool-use trajectories on mathematical datasets, incorporating imitation learning and output space shaping. GPT-4 generated diverse reasoning patterns, and the effectiveness of TORA’s integration of rationales with programs was evaluated. The models excel on ten mathematical reasoning datasets, surpassing open-source models with significant improvements in reasoning and problem-solving.

Advancements in Mathematical Reasoning with TORA

TORA enhances mathematical reasoning by seamlessly integrating natural language reasoning with external tools. By interweaving rationales and program execution, TORA achieves state-of-the-art performance in various mathematical reasoning tasks. The comprehensive analysis of tool interaction benefits and challenges provides valuable insights for future research in developing more advanced reasoning agents.

If you are interested in reading the full research paper or accessing the code on GitHub, you can find them here and here.

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