We introduce DataMorgana, a tool for generating synthetic Q&A benchmarks tailored to RAG applications in enterprise settings. DataMorgana enables customization of the generated benchmark according to the expected diverse traffic of the RAG application. It allows for specifying question types and their associated distribution via a lightweight configuration mechanism. We demonstrate via a series of quantitative and qualitative experiments that DataMorgana surpasses existing tools in terms of lexical, syntactic, and semantic diversity of the generated benchmark while maintaining high quality. We run our experiments over domain-specific and general-knowledge public datasets, as well as two private datasets from governmental RAG applications: one for citizens and the other for government employees. The private datasets have been shared with us by AI71, an AI company, which has integrated DataMorgana into its offerings. In addition, DataMorgana has been offered to about 150 researchers worldwide as part of the SIGIR’2025 LiveRAG Challenge held in Spring 2025.

Retrieval Augmented Generation enhances LLM accuracy by adding passages retrieved from an external corpus to the LLM prompt. This paper investigates how positional bias - the tendency of LLMs to weight information differently based on its position in the prompt - affects not only the LLM’s capability to capitalize on relevant passages, but also its susceptibility to distracting passages. Through extensive experiments on three benchmarks, we show how state-of-the-art retrieval pipelines, while attempting to retrieve relevant passages, systematically bring highly distracting ones to the top ranks, with over 60% of queries containing at least one highly distracting passage among the top-10 retrieved passages. As a result, the impact of the LLM positional bias, which in controlled settings is often reported as very prominent by related works, is actually marginal in real scenarios since both relevant and distracting passages are, in turn, penalized. Indeed, our findings reveal that sophisticated strategies that attempt to rearrange the passages based on LLM positional preferences do not perform better than random shuffling.

Retrieval-Augmented Generation (RAG) enhances LLMs by grounding answers in retrieved passages, which is key in factual Question Answering. However, generated answers may still be unfaithful to the passages, either due to retrieval or generation errors. Many RAG downstream applications rely on assessing answer faithfulness for applying fallback strategies, yet address it implicitly, without a consistent evaluation methodology. We introduce the task of Answering with Faithfulness (AwF), which brings faithfulness prediction to the forefront, explicitly coupling it with answer generation. We define variants of the precision and recall metrics tailored to this task, facilitating direct evaluation and comparison of different AwF methods.We then demonstrate, both theoretically and empirically, that for RAG applications using AwF as a sub-procedure, an improvement to the AwF metrics translates to an improvement to the downstream performance. This results in improved performance for recently published results.

The categorization of massive e-Commerce data is a crucial, well-studied task, which is prevalent in industrial settings. In this work, we aim to improve an existing product categorization model that is already in use by a major web company, serving multiple applications. At its core, the product categorization model is a text classification model that takes a product title as an input and outputs the most suitable category out of thousands of available candidates. Upon a closer inspection, we found inconsistencies in the labeling of similar items. For example, minor modifications of the product title pertaining to colors or measurements majorly impacted the model’s output. This phenomenon can negatively affect downstream recommendation or search applications, leading to a sub-optimal user experience. To address this issue, we propose a new framework for consistent text categorization. Our goal is to improve the model’s consistency while maintaining its production-level performance. We use a semi-supervised approach for data augmentation and presents two different methods for utilizing unlabeled samples. One method relies directly on existing catalogs, while the other uses a generative model. We compare the pros and cons of each approach and present our experimental results.