24 citations · 59 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…