117 citations · 692 across the 53 of their papers we have counts for
53 papers
Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems
Kai Hua, Zhiyuan Feng, Chongyang Tao +2
Recently, knowledge-grounded conversations in the open domain gain great attention from researchers. Existing works on retrieval-based dialogue systems have paid tremendous efforts…
A Survey on Knowledge Distillation of Large Language Models
Xiaohan Xu, Ming Li, Chongyang Tao +6
In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities from leading proprietary LLMs, such…
Good Questions Help Zero-Shot Image Reasoning
Kaiwen Yang, Tao Shen, Xinmei Tian +4
Aligning the recent large language models (LLMs) with computer vision models leads to large vision-language models (LVLMs), which have paved the way for zero-shot image reasoning t…
Thread of Thought Unraveling Chaotic Contexts
Yucheng Zhou, Xiubo Geng, Tao Shen +4
Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation. Neve…
ZC3: Zero-Shot Cross-Language Code Clone Detection
Jia Li, Chongyang Tao, Zhi Jin +2
Developers introduce code clones to improve programming productivity. Many existing studies have achieved impressive performance in monolingual code clone detection. However, durin…
Re-Reading Improves Reasoning in Large Language Models
Xiaohan Xu, Chongyang Tao, Tao Shen +5
To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i.e., \textbf{Re}-\text…
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Haipeng Luo, Qingfeng Sun, Can Xu +8
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, mo…
Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning
Xindi Wang, Yufei Wang, Can Xu +6
Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explici…
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Ziyang Luo, Can Xu, Pu Zhao +7
Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on…
Improving the Robustness of Summarization Systems with Dual Augmentation
Xiuying Chen, Guodong Long, Chongyang Tao +4
A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. In this work, we first explore the…
UMSE: Unified Multi-scenario Summarization Evaluation
Shen Gao, Zhitao Yao, Chongyang Tao +4
Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1) reference-based: evaluating wit…
Augmented Large Language Models with Parametric Knowledge Guiding
Ziyang Luo, Can Xu, Pu Zhao +5
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities. However, their…
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu +4
Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods r…
Synergistic Interplay between Search and Large Language Models for Information Retrieval
Jiazhan Feng, Chongyang Tao, Xiubo Geng +5
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to mode…
Large Language Models are Strong Zero-Shot Retriever
Tao Shen, Guodong Long, Xiubo Geng +3
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retri…
WizardLM: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng +6
Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming a…
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval
Ziyang luo, Pu Zhao, Can Xu +6
Image-text retrieval (ITR) is a task to retrieve the relevant images/texts, given the query from another modality. The conventional dense retrieval paradigm relies on encoding imag…
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
Jiazhan Feng, Qingfeng Sun, Can Xu +5
Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to bett…
Fine-Grained Distillation for Long Document Retrieval
Yucheng Zhou, Tao Shen, Xiubo Geng +4
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a…
Adam: Dense Retrieval Distillation with Adaptive Dark Examples
Chongyang Tao, Chang Liu, Tao Shen +4
To improve the performance of the dual-encoder retriever, one effective approach is knowledge distillation from the cross-encoder ranker. Existing works construct the candidate pas…
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
Yang Li, Canran Xu, Guodong Long +3
Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task…
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation
Xueliang Zhao, Yuxuan Wang, Chongyang Tao +2
We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) th…
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning
Xueliang Zhao, Tingchen Fu, Chongyang Tao +1
Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowled…
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
Tao Shen, Xiubo Geng, Chongyang Tao +5
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latenc…