24 citations · 59 across the 8 of their papers we have counts for
9 papers
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Evaluating Cost-Accuracy Trade-offs in Multimodal Search Relevance Judgements
Silvia Terragni, Hoang Cuong, Joachim Daiber +2
Large Language Models (LLMs) have demonstrated potential as effective search relevance evaluators. However, there is a lack of comprehensive guidance on which models consistently p…
Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems
Ivan Sekulić, Silvia Terragni, Victor Guimarães +5
In the realm of dialogue systems, user simulation techniques have emerged as a game-changer, redefining the evaluation and enhancement of task-oriented dialogue (TOD) systems. Thes…
In-Context Learning User Simulators for Task-Oriented Dialog Systems
Silvia Terragni, Modestas Filipavicius, Nghia Khau +3
This paper presents a novel application of large language models in user simulation for task-oriented dialog systems, specifically focusing on an in-context learning approach. By h…
Contrastive language and vision learning of general fashion concepts
Patrick John Chia, Giuseppe Attanasio, Federico Bianchi +5
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learnin…
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Silvia Terragni, Ismail Harrando, Pasquale Lisena +2
Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, dive…
Contrastive Language-Image Pre-training for the Italian Language
Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni +3
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount…
Cross-lingual Contextualized Topic Models with Zero-shot Learning
Federico Bianchi, Silvia Terragni, Dirk Hovy +2
Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in multiple languages. They all cover the same content, but the linguistic differences make it impossible to use…
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
Federico Bianchi, Silvia Terragni, Dirk Hovy
Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are…