Papers by Vaclav Petricek
Evolutionary Contrastive Distillation for Language Model Alignment (2024.findings-emnlp)
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Julian Katz-Samuels, Zheng Li, Hyokun Yun, Priyanka Nigam, Yi Xu, Vaclav Petricek, Bing Yin, Trishul Chilimbi
| Challenge: | Existing studies indicate that large language models struggle with challenging instructions. |
| Approach: | They propose a method for generating high-quality synthetic preference data to enhance the complex instruction-following capability of language models. |
| Outcome: | The proposed method exceeds the performance of current SOTA 7B models and is competitive even with open-source 70B models. |
Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores (2022.naacl-industry)
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Ashutosh Joshi, Shankar Vishwanath, Choon Teo, Vaclav Petricek, Vishy Vishwanathan, Rahul Bhagat, Jonathan May
| Challenge: | Existing methods to augment training data for e-commerce stores using behavioral data are limited in low-traffic stores . eXtreme multi-label classification systems require large amounts of customer behavior data . |
| Approach: | They propose a technique that augments behavioral training data via query reformulation . they use an example semantic matching model from the e-commerce store AL-XMC . |
| Outcome: | The proposed method improves quality of the AL-XMC model over a baseline model. |
Deep Metric Learning to Hierarchically Rank - An Application in Product Retrieval (2023.emnlp-industry)
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Kee Kiat Koo, Ashutosh Joshi, Nishaanth Reddy, Karim Bouyarmane, Ismail Tutar, Vaclav Petricek, Changhe Yuan
| Challenge: | e-commerce search engines use customer behavior signals to augment lexical matching and improve search relevance. |
| Approach: | They propose a method to identify duplicate and near-duplicate products across stores . they use Hierarchical Ranked Multi Similarity Loss to learn hierarchical metric space . |
| Outcome: | The proposed model outperforms baselines in terms of catalog coverage and precision of the mappings. |