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Articles by Stefano Campese
Query-aware index pruning for retrieval under budget constraints
Production search indices operate under strict storage and latency budgets that prevent indexing the full corpus (e.g.: web documents). When only a fraction of documents can be retained, the system must decide which ones to keep.
Improving document retrieval coherence for semantically equivalent queries
Dense Retrieval (DR) models have proven to be effective for Document Retrieval and Information Grounding tasks. Usually, these models are trained and optimized for improving the relevance of top-ranked documents for a given query. Previous work has shown that popular DR models are sensitive to the query and document lexicon: small variations of it may lead to a significant difference in the set of retrieved documents.
Analyzing and improving coherence of large language models in question answering
Large language models (LLMs) have recently revolutionized natural language processing. These models, however, often suffer from instability or lack of coherence, that is the ability of the models to generate semantically equivalent outputs when receiving diverse yet semantically equivalent input variations.
Exploring coherence of LLMs in multilingual question answering
Recent studies have highlighted that Large Language Models (LLMs) often exhibit limited coherence, that is the ability to produce consistent responses to semantically equivalent questions. While most prior research has focused exclusively on English, limited investigation has been conducted on other languages. In this work, we study the coherence of LLMs on Question Answering tasks across six languages: English, Italian, German, Chinese, Japanese, and Vietnamese.
Pre-training methods for question reranking
One interesting approach to Question Answering (QA) is to search for semantically similar questions, which have been answered before. This task is different from answer retrieval as it focuses on questions rather than only on the answers, therefore it requires different model training on different data. In this work, we introduce a novel unsupervised pre-training method specialized for retrieving and ranking questions.
Contextual answer sentence selection
Coala is a python package for Contextual Answer Sentence Selection. Answer Sentence Selection (AS2) is a sub-task of question answering, and it aims at finding the sentence containing the answer for a given input question from a pool of possible candidate sentences (e.g. retrieved from a search engine). In our contextual AS2 task, we provide models that leverage contextual information coming from the documents (or the paragraphs) containing the candidate answers.
QUADRo: Dataset and models for question-answer database retrieval
An effective approach to design automated Question Answering (QA) systems is to efficiently retrieve answers from pre-computed databases containing question/answer pairs. One of the main challenges to this design is the lack of training/testing data. Existing resources are limited in size and topics and either do not consider answers (question-question similarity only) or their quality in the annotation process.
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