Search Results for author: Fanghua Ye

Found 16 papers, 11 papers with code

Anchor-based Large Language Models

no code implementations12 Feb 2024 Jianhui Pang, Fanghua Ye, Derek F. Wong, Longyue Wang

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.

Computational Efficiency Question Answering

Benchmarking LLMs via Uncertainty Quantification

1 code implementation23 Jan 2024 Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F. Wong, Emine Yilmaz, Shuming Shi, Zhaopeng Tu

The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.

Benchmarking Uncertainty Quantification

Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models

no code implementations16 Jan 2024 Jianhui Pang, Fanghua Ye, Longyue Wang, Dian Yu, Derek F. Wong, Shuming Shi, Zhaopeng Tu

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.

Machine Translation NMT +2

Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking

no code implementations14 Dec 2023 Shitong Sun, Fanghua Ye, Shaogang Gong

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.

Image Retrieval Retrieval +1

Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting

1 code implementation15 Oct 2023 Fanghua Ye, Meng Fang, Shenghui Li, Emine Yilmaz

Furthermore, we propose distilling the rewriting capabilities of LLMs into smaller models to reduce rewriting latency.

Conversational Search Language Modelling +2

Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process

1 code implementation21 May 2023 Fanghua Ye, Zhiyuan Hu, Emine Yilmaz

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

no code implementations ACL 2022 Yue Feng, Aldo Lipani, Fanghua Ye, Qiang Zhang, Emine Yilmaz

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

ASSIST: Towards Label Noise-Robust Dialogue State Tracking

1 code implementation Findings (ACL) 2022 Fanghua Ye, Yue Feng, Emine Yilmaz

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.

Dialogue State Tracking

Slot Self-Attentive Dialogue State Tracking

1 code implementation22 Jan 2021 Fanghua Ye, Jarana Manotumruksa, Qiang Zhang, Shenghui Li, Emine Yilmaz

Then a stacked slot self-attention is applied on these features to learn the correlations among slots.

Dialogue State Tracking Task-Oriented Dialogue Systems

Auto-weighted Robust Federated Learning with Corrupted Data Sources

1 code implementation14 Jan 2021 Shenghui Li, Edith Ngai, Fanghua Ye, Thiemo Voigt

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.

Federated Learning Privacy Preserving

Outlier-Resilient Web Service QoS Prediction

1 code implementation1 Jun 2020 Fanghua Ye, Zhiwei Lin, Chuan Chen, Zibin Zheng, Hong Huang

The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates.

Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection

2 code implementations CIKM 2018 Fanghua Ye, Chuan Chen, Zibin Zheng

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.

Local Community Detection Network Community Partition +2

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