Papers with external retrieval
Leveraging Product Catalog Patterns for Multilingual E-commerce Product Attribute Prediction (2025.emnlp-industry)
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| Challenge: | E-commerce stores increasingly use Large Language Models to improve catalog data quality . a critical challenge is accurately predicting missing structured attribute values . |
| Approach: | They propose a retrieval-augmented system that leverages existing product catalog entries to guide LLM predictions for missing attributes. |
| Outcome: | The proposed system improves catalog data quality by 34% and accuracy by 0.8% . the proposed model can predict missing attributes in multilingual product catalogs . |
CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation (2025.findings-emnlp)
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| Challenge: | Multilingual Large Language Models (MLLMs) exhibit strong generalization across languages, yet they remain prone to hallucinations due to training data imbalances. |
| Approach: | They propose a cross-lingual Chain-of-Thought framework that enhances cross-linguistic alignment . the framework guides the model to reason in a high-resource language before generating answers in low-resourced language. |
| Outcome: | The proposed framework reduces hallucination rates by up to 62% and significantly improves factual knowledge transfer across language pairs. |
Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs (2026.acl-long)
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| Challenge: | Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse relies on external retrieval, which is similarity-based, can introduce noise, and adds latency. |
| Approach: | They propose a lightweight plug-in that stores experience in its parameters and generates a structured, instance-tailored experience entry in a single forward pass to guide a frozen LLM executor. |
| Outcome: | Experiments on mathematical reasoning benchmarks show consistent accuracy gains across executors with low overhead. |