26 citations · 123 across the 17 of their papers we have counts for
17 papers
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback
Qian Dong, Yiding Liu, Qingyao Ai +6
Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR). However, the responses ge…
Explainability for Large Language Models: A Survey
Haiyan Zhao, Hanjie Chen, Fan Yang +6
Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transpa…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Zhikai Chen, Haitao Mao, Hang Li +8
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily…
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
Canjia Li, Xiaoyang Wang, Dongdong Li +6
Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success…
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Juanhui Li, Harry Shomer, Haitao Mao +5
Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt…
I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
Qian Dong, Yiding Liu, Qingyao Ai +5
Passage retrieval is a fundamental task in many information systems, such as web search and question answering, where both efficiency and effectiveness are critical concerns. In re…
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Yubao Tang, Ruqing Zhang, Jiafeng Guo +5
Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries…
Boosting Event Extraction with Denoised Structure-to-Text Augmentation
bo wang, Heyan Huang, Xiaochi Wei +6
Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving…
Learning to Tokenize for Generative Retrieval
Weiwei Sun, Lingyong Yan, Zheng Chen +7
Conventional document retrieval techniques are mainly based on the index-retrieve paradigm. It is challenging to optimize pipelines based on this paradigm in an end-to-end manner.…
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
Weiwei Sun, Lingyong Yan, Xinyu Ma +5
Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization across various language-related tasks, including search engines. However, existing work utilizes…
Layout-aware Webpage Quality Assessment
Anfeng Cheng, Yiding Liu, Weibin Li +6
Identifying high-quality webpages is fundamental for real-world search engines, which can fulfil users' information need with the less cognitive burden. Early studies of \emph{webp…
Feature-Level Debiased Natural Language Understanding
Yougang Lyu, Piji Li, Yechang Yang +5
Natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance on specific datasets. As a result,…
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
Lianshang Cai, Linhao Zhang, Dehong Ma +6
Pre-trained language models have become a crucial part of ranking systems and achieved very impressive effects recently. To maintain high performance while keeping efficient comput…
CPS-MEBR: Click Feedback-Aware Web Page Summarization for Multi-Embedding-Based Retrieval
Wenbiao Li, Pan Tang, Zhengfan Wu +7
Embedding-based retrieval (EBR) is a technique to use embeddings to represent query and document, and then convert the retrieval problem into a nearest neighbor search problem in t…
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
Juanhui Li, Harry Shomer, Jiayuan Ding +5
Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completio…
A Simple yet Effective Framework for Active Learning to Rank
Qingzhong Wang, Haifang Li, Haoyi Xiong +7
While China has become the biggest online market in the world with around 1 billion internet users, Baidu runs the world largest Chinese search engine serving more than hundreds of…