Multi-Step Semantic Reasoning in Generative Retrieval
arXiv:2603.12368v1 Announce Type: cross Abstract: Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports, due to limited reasoning capabilities. This limitation leads to suboptimal retrieval accuracy and hinders practical applicability. We propose ReasonGR, a framework designed to enhance multi-step semantic reasoning in numerical contexts within GR. ReasonGR employs a structured prompting strategy combining task-specific instructions with stepwise reasoning guidance to better address complex retrieval queries. Additionally, it integrates a reasoning-focused adaptation module to improve the learning of reasoning-related parameters. Experiments on the FinQA dataset, which contains financial queries over comple
arXiv:2603.12368v1 Announce Type: cross Abstract: Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semantic reasoning over financial reports, due to limited reasoning capabilities. This limitation leads to suboptimal retrieval accuracy and hinders practical applicability. We propose ReasonGR, a framework designed to enhance multi-step semantic reasoning in numerical contexts within GR. ReasonGR employs a structured prompting strategy combining task-specific instructions with stepwise reasoning guidance to better address complex retrieval queries. Additionally, it integrates a reasoning-focused adaptation module to improve the learning of reasoning-related parameters. Experiments on the FinQA dataset, which contains financial queries over complex documents, demonstrate that ReasonGR improves retrieval accuracy and consistency, indicating its potential for advancing GR models in reasoning-intensive retrieval scenarios.
Executive Summary
The article proposes ReasonGR, a framework designed to enhance multi-step semantic reasoning in numerical contexts within Generative Retrieval (GR) models. ReasonGR addresses the limitation of existing GR models in handling complex queries, particularly in numerical contexts such as financial reports. The framework employs a structured prompting strategy and a reasoning-focused adaptation module to improve retrieval accuracy and consistency. Experiments on the FinQA dataset demonstrate the potential of ReasonGR in advancing GR models in reasoning-intensive retrieval scenarios.
Key Points
- ▸ ReasonGR framework for enhancing multi-step semantic reasoning in GR models
- ▸ Structured prompting strategy for complex retrieval queries
- ▸ Reasoning-focused adaptation module for improving learning of reasoning-related parameters
Merits
Improved Retrieval Accuracy
ReasonGR demonstrates improved retrieval accuracy and consistency in experiments on the FinQA dataset.
Demerits
Limited Generalizability
The framework's effectiveness may be limited to specific domains or datasets, requiring further testing and validation.
Expert Commentary
The proposal of ReasonGR marks a significant step forward in addressing the limitations of GR models in handling complex queries. The framework's ability to enhance multi-step semantic reasoning has important implications for a range of applications, from financial analysis to legal research. However, further research is needed to fully realize the potential of ReasonGR and to address potential limitations and challenges. As AI models become increasingly ubiquitous, it is essential to prioritize transparency, explainability, and accountability in their development and deployment.
Recommendations
- ✓ Further testing and validation of ReasonGR on diverse datasets and domains
- ✓ Investigation into the potential applications and implications of ReasonGR in other fields, such as legal research and healthcare