Language models (LMs) have revolutionized the way we interact with
infor...
As a primary means of information acquisition, information retrieval (IR...
Video moment localization aims to retrieve the target segment of an untr...
Text-to-motion generation has gained increasing attention, but most exis...
Human motion prediction (HMP) has emerged as a popular research topic du...
Conversational recommender systems (CRS) aim to provide the recommendati...
The research field of Information Retrieval (IR) has evolved significant...
Despite the superior performance, Large Language Models (LLMs) require
s...
Although pre-trained language models (PLMs) have recently advanced the
r...
Although Large Language Models (LLMs) have demonstrated extraordinary
ca...
Conversational recommender systems (CRSs) aim to provide recommendation
...
Chain-of-thought prompting (CoT) and tool augmentation have been validat...
In this paper, we propose a novel language model guided captioning appro...
People often imagine relevant scenes to aid in the writing process. In t...
Although large language models (LLMs) have achieved excellent performanc...
The recent success of large language models (LLMs) has shown great poten...
Large language models (LLMs), such as ChatGPT, are prone to generate
hal...
Large language models (LLMs) encode a large amount of world knowledge.
H...
Inspired by the superior language abilities of large language models (LL...
In this paper, we study how to improve the zero-shot reasoning ability o...
In the past decades, recommender systems have attracted much attention i...
Recently, continuous diffusion models (CDM) have been introduced into
no...
Diffusion models have become a new generative paradigm for text generati...
Learning effective high-order feature interactions is very crucial in th...
In this paper, we introduce a new NLP task – generating short factual
ar...
In this paper, we propose a highly parameter-efficient approach to scali...
Non-autoregressive (NAR) text generation has attracted much attention in...
To facilitate research on text generation, this paper presents a
compreh...
Although pre-trained language models (PLMs) have shown impressive perfor...
Dense retrieval aims to map queries and passages into low-dimensional ve...
Multi-hop Question Answering over Knowledge Graph (KGQA) aims to find th...
Deep semantic matching aims to discriminate the relationship between
doc...
With the growth of high-dimensional sparse data in web-scale recommender...
We study the text generation task under the approach of pre-trained lang...
Sampling proper negatives from a large document pool is vital to effecti...
To develop effective and efficient graph similarity learning (GSL) model...
While self-supervised learning techniques are often used to mining impli...
Users' search tasks have become increasingly complicated, requiring mult...
Document retrieval has been extensively studied within the index-retriev...
We propose a video feature representation learning framework called STAR...
As an essential operation of legal retrieval, legal case matching plays ...
Pre-trained language models (PLMs) have achieved notable success in natu...
Conversational recommender systems (CRS) aim to proactively elicit user
...
In order to support the study of recent advances in recommender systems,...
This paper aims to advance the mathematical intelligence of machines by
...
In order to develop effective sequential recommenders, a series of seque...
Relevant recommendation is a special recommendation scenario which provi...
The learn-to-compare paradigm of contrastive representation learning (CR...
Commonsense reasoning in natural language is a desired ability of artifi...
Pretrained language models (PLMs) have made remarkable progress in text
...