47 citations · 153 across the 19 of their papers we have counts for
20 papers
Tracing Query Expansion Effects through Sparse Autoencoder Features
Fangan Dong, Weiran Shi, Zhiwei Xu +5
Query expansion (QE) is a critical technique in information retrieval that enriches underspecified queries with additional textual context. However, its effect is often unreliable…
Beyond Local Edits: Embedding-Virtualized Knowledge for Broader Evaluation and Preservation of Model Editing
Shuainan Liu, Xuanang Chen, Ben He +1
Knowledge editing methods for large language models are commonly evaluated using predefined benchmarks that assess edited facts together with a limited set of related or neighborin…
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
Ying Zhou, Ben He, Le Sun
With the development of large language models (LLMs), detecting whether text is generated by a machine becomes increasingly challenging in the face of malicious use cases like the…
Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
Xiaoyang Chen, Ben He, Hongyu Lin +5
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercus…
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Ning Bian, Xianpei Han, Hongyu Lin +3
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning…
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection
Xinlin Peng, Ying Zhou, Ben He +2
Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks. However, the utilization of these models carries inherent risks, including but not lim…
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Xinyan Guan, Yanjiang Liu, Hongyu Lin +4
Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs). Existing methods usually on…
Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval
Xueru Wen, Xiaoyang Chen, Xuanang Chen +2
Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass r…
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection
Xuanang Chen, Ben He, Le Sun +1
Neural ranking models (NRMs) have undergone significant development and have become integral components of information retrieval (IR) systems. Unfortunately, recent research has un…
Understanding Differential Search Index for Text Retrieval
Xiaoyang Chen, Yanjiang Liu, Ben He +2
The Differentiable Search Index (DSI) is a novel information retrieval (IR) framework that utilizes a differentiable function to generate a sorted list of document identifiers in r…
Towards Imperceptible Document Manipulations against Neural Ranking Models
Xuanang Chen, Ben He, Zheng Ye +2
Adversarial attacks have gained traction in order to identify potential vulnerabilities in neural ranking models (NRMs), but current attack methods often introduce grammatical erro…
Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns
Ning Bian, Hongyu Lin, Peilin Liu +5
Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs),…
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
Ning Bian, Xianpei Han, Le Sun +5
Large language models (LLMs) have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain poi…
Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective
Ying Zhou, Xuanang Chen, Ben He +2
Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph. Current KGC research mostly follows an entity ranking protocol,…
Groupwise Query Performance Prediction with BERT
Xiaoyang Chen, Ben He, Le Sun
While large-scale pre-trained language models like BERT have advanced the state-of-the-art in IR, its application in query performance prediction (QPP) is so far based on pointwise…
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision
Ning Bian, Xianpei Han, Bo Chen +3
Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained lang…
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
Xiaoyang Chen, Kai Hui, Ben He +3
BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independent…
Simplified TinyBERT: Knowledge Distillation for Document Retrieval
Xuanang Chen, Ben He, Kai Hui +2
Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses. To this end, this paper first empirica…
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Zhi Zheng, Kai Hui, Ben He +3
Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant info…
PARADE: Passage Representation Aggregation for Document Reranking
Canjia Li, Andrew Yates, Sean MacAvaney +2
Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking. Due to inherent sequence length limits of these models…