Papers with unified system
Answering Open-Domain Questions of Varying Reasoning Steps from Text (2021.emnlp-main)
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| Challenge: | a new benchmark is developed to answer open-domain questions from text . the system uses a single multi-task transformer model to perform all the necessary subtasks . |
| Approach: | They develop a unified system to answer directly from open-domain questions . they use a single multi-task transformer model to perform all the necessary subtasks . |
| Outcome: | The proposed system can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. |
Co-Teaching Student-Model through Submission Results of Shared Task (2021.findings-emnlp)
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| Challenge: | Shared tasks require participants to submit only system outputs and descriptions. |
| Approach: | They propose to utilize all system outputs in a shared task to build a unified system that performs better than the task's single best system. |
| Outcome: | The proposed scheme outperforms the best system in the SHINRA2019-JP shared task with nine participants. |
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models (2025.acl-long)
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| Challenge: | Recent advances in large language models and the abundance of food data have led to studies to improve food understanding using LLMs. |
| Approach: | They propose a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generate recipes with associated micro-nutritional information. |
| Outcome: | The proposed system outperforms existing methods in food recommendation, recipe generation and nutritional analysis. |
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent (2026.findings-acl)
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| Challenge: | Existing approaches to individualized glucose regulation are generic and do not account for individual-specific glucose dynamics. |
| Approach: | They propose a physio-feedback agentic loop that integrates individualized absorption modeling with dietary intervention to regulate glucose response. |
| Outcome: | The proposed system improves prediction accuracy and reduces glucose excursions. |