Enhancing Action and Ingredient Modeling for Semantically Grounded Recipe Generation
arXiv:2602.15862v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically incorrect actions or ingredients despite high lexical scores (e.g., BLEU, ROUGE). To address this gap, we propose a semantically grounded framework that predicts and validates actions and ingredients as internal context for instruction generation. Our two-stage pipeline combines supervised fine-tuning (SFT) with reinforcement fine-tuning (RFT): SFT builds foundational accuracy using an Action-Reasoning dataset and ingredient corpus, while RFT employs frequency-aware rewards to improve long-tail action prediction and ingredient generalization. A Semantic Confidence Scoring and Rectification (SCSR) module further filters and corrects predictions. Experiments on Recipe1M show state-of-the-art performance and markedly improved semantic fidelity.
arXiv:2602.15862v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically incorrect actions or ingredients despite high lexical scores (e.g., BLEU, ROUGE). To address this gap, we propose a semantically grounded framework that predicts and validates actions and ingredients as internal context for instruction generation. Our two-stage pipeline combines supervised fine-tuning (SFT) with reinforcement fine-tuning (RFT): SFT builds foundational accuracy using an Action-Reasoning dataset and ingredient corpus, while RFT employs frequency-aware rewards to improve long-tail action prediction and ingredient generalization. A Semantic Confidence Scoring and Rectification (SCSR) module further filters and corrects predictions. Experiments on Recipe1M show state-of-the-art performance and markedly improved semantic fidelity.
Executive Summary
This article proposes a semantically grounded framework for recipe generation, addressing the issue of semantically incorrect actions or ingredients in existing models. The framework combines supervised fine-tuning and reinforcement fine-tuning with a semantic confidence scoring and rectification module. Experiments on Recipe1M show state-of-the-art performance and improved semantic fidelity. The approach has the potential to improve the accuracy and reliability of recipe generation models, particularly in applications where precise instructions are crucial. However, the article's focus on a specific dataset and task may limit its broader applicability and transferability to other domains.
Key Points
- ▸ The proposed framework combines supervised fine-tuning and reinforcement fine-tuning with a semantic confidence scoring and rectification module.
- ▸ The approach improves semantic fidelity in recipe generation, particularly for long-tail action prediction and ingredient generalization.
- ▸ Experiments on Recipe1M demonstrate state-of-the-art performance and improved accuracy compared to existing models.
Merits
Improved Semantic Fidelity
The proposed framework addresses the issue of semantically incorrect actions or ingredients in existing recipe generation models, resulting in improved semantic fidelity and accuracy.
State-of-the-Art Performance
Experiments on Recipe1M demonstrate that the proposed framework achieves state-of-the-art performance, outperforming existing models on this task.
Flexibility and Customizability
The framework's modular design allows for flexibility and customizability, enabling researchers and practitioners to adapt and extend the approach to suit their specific needs and goals.
Demerits
Limited Broader Applicability
The article's focus on a specific dataset and task may limit the broader applicability and transferability of the proposed framework to other domains and tasks.
Dependence on High-Quality Training Data
The framework's performance relies heavily on the quality and quantity of the training data, which may be a limitation in real-world applications where high-quality data may be scarce or difficult to obtain.
Potential Overfitting
The use of reinforcement fine-tuning and semantic confidence scoring and rectification may increase the risk of overfitting, particularly if the training data is limited or biased.
Expert Commentary
The proposed framework is a significant advancement in the field of recipe generation and multimodal processing. By combining supervised fine-tuning and reinforcement fine-tuning with a semantic confidence scoring and rectification module, the authors have developed a robust and accurate approach to predicting actions and ingredients in recipes. While the article's focus on a specific dataset and task may limit its broader applicability and transferability, the framework's modular design and potential for customizability make it an attractive solution for researchers and practitioners. As the field of multimodal processing continues to evolve, it will be essential to explore the applications and implications of this framework in various domains and tasks.
Recommendations
- ✓ Future research should focus on adapting the proposed framework to other domains and tasks, such as text generation, image captioning, and multimodal summarization.
- ✓ Investigations into the framework's performance on diverse and challenging datasets, as well as its robustness to noise and bias in training data, would be valuable additions to the existing literature.
