Search Results for author:
Found 16 papers, 13 papers with code
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches.
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Node Classification
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Benchmarking LLMs via Uncertainty Quantification
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation
The annotations in the training set remain unchanged (same as MultiWOZ 2. 1) to elicit robust and noise-resilient model training.
Slot Self-Attentive Dialogue State Tracking
Then a stacked slot self-attention is applied on these features to learn the correlations among slots.
ASSIST: Towards Label Noise-Robust Dialogue State Tracking
In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels.
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling
Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes.
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models (LLMs): domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search.
MetaASSIST: Robust Dialogue State Tracking with Meta Learning
Experimental results demonstrate that all three schemes can achieve competitive performance.
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates.
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting
Furthermore, we propose distilling the rewriting capabilities of LLMs into smaller models to reduce rewriting latency.
Auto-weighted Robust Federated Learning with Corrupted Data Sources
In this paper, we address this challenge by proposing Auto-weighted Robust Federated Learning (arfl), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources.
Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process
It assumes that the performance of a dialogue system can be measured by user satisfaction and uses an estimator to simulate users.
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Turn-Level Active Learning for Dialogue State Tracking
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems.
Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking
Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text.
Anchor-based Large Language Models
Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation.
