Papers by Guy Horowitz
Do RAG Systems Really Suffer From Positional Bias? (2025.emnlp-main)
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| Challenge: | Retrieval Augmented Generation (RAG) improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
| Approach: | They propose to use a retrieval algorithm to add passages from an external corpus to the LLM prompt to improve the factual accuracy of LLMs. |
| Outcome: | The proposed approach improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
Consistent Text Categorization using Data Augmentation in e-Commerce (2023.acl-industry)
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| Challenge: | Upon closer inspection, we found inconsistencies in the labeling of similar items. |
| Approach: | They propose to improve an existing product categorization model that takes a product title as input and outputs the most suitable category out of thousands of available candidates. |
| Outcome: | The proposed model is based on a product title and outputs the most suitable category out of thousands of available candidates. |
Generating Q&A Benchmarks for RAG Evaluation in Enterprise Settings (2025.acl-industry)
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| Challenge: | DataMorgana generates synthetic Q&A benchmarks tailored to RAG applications . lexical, syntactic, and semantic diversity of generated benchmarks exceeds existing tools . |
| Approach: | They propose a tool for generating synthetic Q&A benchmarks tailored to RAG applications in enterprise settings. |
| Outcome: | The proposed tool surpasses existing tools in terms of lexical, syntactic, and semantic diversity while maintaining high quality. |