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Found 13 papers, 8 papers with code
An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures
Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents.
Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models
Topic models have been widely used to discover hidden topics in a collection of documents.
BETOLD: A Task-Oriented Dialog Dataset for Breakdown Detection
Ideally TOD systems should be able to detect dialog breakdowns to prevent users from quitting a conversation and to encourage them to interact with the system again.
Evaluating Cost-Accuracy Trade-offs in Multimodal Search Relevance Judgements
Large Language Models (LLMs) have demonstrated potential as effective search relevance evaluators.
Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems
Notably, we have observed that fine-tuning enhances the simulator's coherence with user goals, effectively mitigating hallucinations -- a major source of inconsistencies in simulator responses.
In-Context Learning User Simulators for Task-Oriented Dialog Systems
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.
Contrastive language and vision learning of general fashion concepts
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models.
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Topic models are statistical methods that extract underlying topics from document collections.
Contrastive Language-Image Pre-training for the Italian Language
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts.
OCTIS: Comparing and Optimizing Topic models is Simple!
In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach.
Cross-lingual Contextualized Topic Models with Zero-shot Learning
They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data.
Constrained Relational Topic Models
Relational topic models (RTM) have been widely used to discover hidden topics in a collection of networked documents.
