- The paper demonstrates a neuro-symbolic training framework that embeds logical constraints into LLM training to boost spatial reasoning.
- It utilizes a dual Primal-Dual program (via DomiKnowS) to balance cross-entropy loss with logical constraint violations, improving multi-hop reasoning.
- Experimental evaluations across SPARTQA-HUMAN, ResQ, and STEPGAME benchmarks show that the approach significantly outperforms conventional fine-tuning methods.
Neuro-symbolic Training for Reasoning over Spatial Language
Introduction
The paper "Neuro-symbolic Training for Reasoning over Spatial Language" (2406.13828) addresses the limitations of current LLMs in handling complex spatial reasoning tasks. Although LLMs have demonstrated impressive performance on various benchmarks, they often struggle with tasks requiring sophisticated reasoning, particularly spatial reasoning, which is crucial for applications in robotics, computer vision, and language grounding. The paper proposes a neuro-symbolic approach that integrates logical reasoning into the training process of LLMs, aiming to improve their generalizability and transfer learning capabilities.
Methodology
The proposed approach leverages neuro-symbolic techniques to embed logical constraints into the training of LLMs, specifically targeting spatial reasoning tasks. The authors introduce a framework for fine-tuning LLMs, such as BERT and Flan-T5, with an additional supervisory signal derived from logical rules. This process involves minimizing both the cross-entropy loss and the violation of logical constraints, thereby pushing the models towards more effective abstractions.
The methodology is implemented using a dual formulation of the original loss, known as the Primal-Dual program, facilitated by the DomiKnowS framework. Importantly, the approach allows for the integration of partial logical knowledge without requiring full access during inference, making it suitable for real-time applications.
Experimental Evaluation
The research evaluates the effectiveness of the neuro-symbolic training approach on several benchmarks, including SPARTQA-HUMAN, ResQ, and STEPGAME. These datasets present varying degrees of complexity and reasoning depth, from realistic domains requiring commonsense knowledge to synthetic environments demanding elaborate multi-hop reasoning.
Empirical results demonstrate that models trained with the neuro-symbolic technique consistently outperform baselines, especially in scenarios involving deeper reasoning steps. The use of logical constraints during training enhances the reasoning capabilities of models, as evidenced by superior performance on complex spatial question-answering tasks compared to standard fine-tuning approaches.
In-Context Learning and LLMs
The paper also explores the performance of state-of-the-art LLMs, such as GPT-3.5, GPT-4, and Llama3, using in-context learning techniques. These models are evaluated with various prompt engineering strategies, including Chain-of-Thought (CoT) and Chain-of-Symbol (CoS) approaches. While LLMs excel in commonsense reasoning, their performance in multi-hop spatial reasoning remains limited compared to the proposed neuro-symbolic models.
Conclusion
The integration of logical reasoning into the training of LLMs significantly enhances their spatial reasoning capabilities. By enabling models to learn from neuro-symbolic constraints, the researchers offer a compelling solution for improving generalization in tasks involving complex, multi-step reasoning. The methods and findings presented in this paper have important implications for the development of more reliable and human-like intelligent systems capable of robust spatial reasoning. Future research could further explore the application of this approach to other domains and reasoning tasks, potentially combining it with larger models to overcome their inherent limitations in logical abstractions.
