Search Results for author: Zeming Liu

Found 7 papers, 5 papers with code

A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models

no code implementations21 Feb 2024 Boyang Xue, Hongru Wang, Weichao Wang, Rui Wang, Sheng Wang, Zeming Liu, Kam-Fai Wong

The tendency of Large Language Models to generate hallucinations and exhibit overconfidence in predictions raises concerns regarding their reliability.

Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation

1 code implementation30 Dec 2023 Can Chen, Hao liu, Zeming Liu, Xue Liu, Dejing Dou

In this paper, we propose Dual-space Hierarchical Learning (DHL) to leverage multi-level goal sequences and their hierarchical relationships for conversational recommendation.

Recommendation Systems Representation Learning

MidMed: Towards Mixed-Type Dialogues for Medical Consultation

1 code implementation5 Jun 2023 Xiaoming Shi, Zeming Liu, Chuan Wang, Haitao Leng, Kui Xue, Xiaofan Zhang, Shaoting Zhang

To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat.

Dialogue Generation

Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain

1 code implementation10 Nov 2022 Mo Wang, Kexin Lou, Zeming Liu, Pengfei Wei, Quanying Liu

In this paper, we propose a general framework called multi-objective optimization via evolutionary algorithms (MOVEA) to address the non-convex optimization problem in designing TES strategies without predefined direction.

Evolutionary Algorithms

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation

1 code implementation EMNLP 2021 Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che

In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2. 0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation.

Towards Conversational Recommendation over Multi-Type Dialogs

2 code implementations ACL 2020 Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e. g., QA) to a recommendation dialog, taking into account user's interests and feedback.

Vocal Bursts Type Prediction

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