Papers with cross-encoder models
Rationale-Guided Distillation for E-Commerce Relevance Classification: Bridging Large Language Models and Lightweight Cross-Encoders (2025.coling-industry)
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| Challenge: | Large-scale e-commerce search systems typically follow a multi-step process to retrieve relevant products for a given query. |
| Approach: | They propose a distillation approach that uses "rationales" generated by Large Language Models to guide smaller cross-encoder models. |
| Outcome: | The proposed model achieves ROC-AUC improvements of 1.4% on 9 multilingual e-commerce datasets, 2.4% on 3 ESCI datasets and 6% on GLUE datasets while being 50 times faster per sample. |
CELI: Simple yet Effective Approach to Enhance Out-of-Domain Generalization of Cross-Encoders. (2024.naacl-short)
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| Challenge: | Existing cross-encoders do not capture all information into the [CLS] token . Xiong et al., 2021) find that the out-of-domain approach is less robust. |
| Approach: | They introduce a cross-encoder with late interaction that incorporates a late interaction layer into existing models. |
| Outcome: | The proposed method improves BEIR by 5% without compromising in-domain effectiveness or search latency. |
A large-scale computational study of content preservation measures for text style transfer and paraphrase generation (2022.acl-srw)
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| Challenge: | Text style transfer and paraphrases generation are growing areas of NLP . many researchers still use BLEU-like measures to evaluate content preservation . |
| Approach: | They compare 57 different measures based on different principles on 19 annotated datasets . they find that measures relying on cross-encoder models outperform alternative approaches . |
| Outcome: | The proposed methods outperform traditional methods on 19 datasets. |
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing (2023.findings-emnlp)
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| Challenge: | CASENT predicts ultra-fine entities mentioned in text into types with calibrated confidence scores. |
| Approach: | They propose a model that predicts ultra-fine entities with calibrated confidence scores for entity typing. |
| Outcome: | The proposed model outperforms existing models in terms of F1 score and calibration error while achieving 50 times faster inference speed. |