Search Results for author: Yue Feng

Found 20 papers, 11 papers with code

Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge

no code implementations Findings (NAACL) 2022 Yue Feng, Zhen Han, Mingming Sun, Ping Li

DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question.

Decoder Open-Domain Question Answering

Chain of Tools: Large Language Model is an Automatic Multi-tool Learner

no code implementations26 May 2024 Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Suzan Verberne, Zhaochun Ren

Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, empowering them to solve practical tasks.

Language Modelling Large Language Model

Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals

no code implementations16 Sep 2023 Zhiyuan Hu, Yue Feng, Yang Deng, Zekun Li, See-Kiong Ng, Anh Tuan Luu, Bryan Hooi

Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios.

Dialogue Generation Language Modelling +3

Dynamic Mesh-Aware Radiance Fields

1 code implementation ICCV 2023 Yi-Ling Qiao, Alexander Gao, Yiran Xu, Yue Feng, Jia-Bin Huang, Ming C. Lin

Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline.

A Large Language Model Enhanced Conversational Recommender System

no code implementations11 Aug 2023 Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun

For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.

Language Modelling Large Language Model +2

Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps

1 code implementation11 Jul 2023 Fuxiao Liu, Paiheng Xu, Zongxia Li, Yue Feng, Hyemi Song

We investigate the role of various demonstration components in the in-context learning (ICL) performance of large language models (LLMs).

In-Context Learning Sentiment Analysis

ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience

no code implementations3 Jul 2023 Ruiyun Xu, Yue Feng, Hailiang Chen

In this study, we investigate the differences in user behavior when employing search engines and chatbot tools for information-seeking tasks.

Chatbot Fact Checking +4

Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System

1 code implementation16 Jun 2023 Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani

This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.

Language Modelling Large Language Model

PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation

1 code implementation14 Jun 2023 Zhiyuan Hu, Chumin Liu, Yue Feng, Anh Tuan Luu, Bryan Hooi

Controllable text generation is a challenging and meaningful field in natural language generation (NLG).

Denoising Sentence +1

Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues

1 code implementation26 May 2023 Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai

Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes.

Attribute Language Modelling +1

A Survey on Asking Clarification Questions Datasets in Conversational Systems

1 code implementation25 May 2023 Hossein A. Rahmani, Xi Wang, Yue Feng, Qiang Zhang, Emine Yilmaz, Aldo Lipani

The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation.

A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering

no code implementations18 Mar 2023 Zhen Han, Yue Feng, Mingming Sun

Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices.

Multiple-choice Question Answering +1

Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access

1 code implementation10 Dec 2022 Yue Feng, Gerasimos Lampouras, Ignacio Iacobacci

To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses.

Response Generation Sentence +1

Learning to Execute Actions or Ask Clarification Questions

1 code implementation Findings (NAACL) 2022 Zhengxiang Shi, Yue Feng, Aldo Lipani

In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions.

Learning to Execute

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.

Decoder Dialogue State Tracking +2

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

Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts

no code implementations ACL 2020 Jingyuan Zhang, Mingming Sun, Yue Feng, Ping Li

Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.

Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction

no code implementations29 Apr 2019 Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.

Attribute Open Information Extraction +3

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