44 citations · 167 across the 30 of their papers we have counts for
30 papers
ITER: Interaction-Aware Retrieval for Agentic Search
Haodong Chen, Shuai Wang, Yu Yin +3
Deep-research agents answer complex user questions through an iterative sequence of search steps, where the agent autonomously formulates sub-queries to retrieve the evidence neede…
Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Search Agents
Shuai Wang, Haodong Chen, Yu Yin +3
Existing deep-search agents use a Search-Visit workflow that retrieves whole webpages without considering the structure they expose through titles, headings, sections, and metadata…
Reproducing LightMem: When Does Memory Construction Help Over Raw-Turn Retrieval?
Yongjie Zhou, Shuai Wang, Bevan Koopman +1
Long-term conversational agents require access to information from earlier interactions, such as a user's preferences, past requests, or previously mentioned facts. Repeatedly prov…
NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs
Shuaidi Wang, Zhan Zhuang, Ruping Huang +1
Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative paradigm. Given the prohibitive computational cost of full fine-tuning, Parameter-…
Can It Reach the Generator? Investigating the Survival of Prompt-Injection Attacks in Realistic RAG Settings
Yu Yin, Shuai Wang, Bevan Koopman +1
Recent generative engine optimisation (GEO) research has shown that prompt-injection attacks can push a target product to the top of an LLM's recommendation list, with the stronges…
DiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models
Shuai Wang, Yu Yin, Shengyao Zhuang +2
This paper shows how diffusion language models (DLMs) can be used as effective and efficient retrievers. Existing DLM-based retrievers (e.g., DiffEmbed) follow BERT-style encoding,…
Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Chunking Strategies for Information Retrieval
Yongjie Zhou, Shuai Wang, Bevan Koopman +1
Document chunking is a critical preprocessing step in dense retrieval systems, yet the design space of chunking strategies remains poorly understood. Recent research has proposed s…
The Vulnerability of LLM Rankers to Prompt Injection Attacks
Yu Yin, Shuai Wang, Bevan Koopman +1
Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within a candidate document (i.e., jailb…
AutoBool: An Reinforcement-Learning trained LLM for Effective Automated Boolean Query Generation for Systematic Reviews
Shuai Wang, Harrisen Scells, Bevan Koopman +1
We present AutoBool, a reinforcement learning (RL) framework that trains large language models (LLMs) to generate effective Boolean queries for medical systematic reviews. Boolean…
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews
Shuai Wang, Harrisen Scells, Bevan Koopman +1
Systematic reviews are comprehensive literature reviews that address highly focused research questions and represent the highest form of evidence in medicine. A critical step in th…
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
Zheng Yao, Shuai Wang, Guido Zuccon
Dense retrievers utilize pre-trained backbone language models (e.g., BERT, LLaMA) that are fine-tuned via contrastive learning to perform the task of encoding text into sense repre…
2D Matryoshka Training for Information Retrieval
Shuai Wang, Shengyao Zhuang, Bevan Koopman +1
2D Matryoshka Training is an advanced embedding representation training approach designed to train an encoder model simultaneously across various layer-dimension setups. This metho…
Starbucks-v2: Improved Training for 2D Matryoshka Embeddings
Shengyao Zhuang, Shuai Wang, Fabio Zheng +2
2D Matryoshka training enables a single embedding model to generate sub-network representations across different layers and embedding dimensions, offering adaptability to diverse c…
Context Embeddings for Efficient Answer Generation in RAG
David Rau, Shuai Wang, Hervé Déjean +1
Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to th…
BERGEN: A Benchmarking Library for Retrieval-Augmented Generation
David Rau, Hervé Déjean, Nadezhda Chirkova +4
Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. In response to the recent popularity of generative LLMs, many RAG approaches have be…
An Investigation of Prompt Variations for Zero-shot LLM-based Rankers
Shuoqi Sun, Shengyao Zhuang, Shuai Wang +1
We provide a systematic understanding of the impact of specific components and wordings used in prompts on the effectiveness of rankers based on zero-shot Large Language Models (LL…
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang +1
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing upt…
Large Language Models for Stemming: Promises, Pitfalls and Failures
Shuai Wang, Shengyao Zhuang, Guido Zuccon
Text stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. The use of stemming in IR has been shown to o…
Zero-shot Generative Large Language Models for Systematic Review Screening Automation
Shuai Wang, Harrisen Scells, Shengyao Zhuang +3
Systematic reviews are crucial for evidence-based medicine as they comprehensively analyse published research findings on specific questions. Conducting such reviews is often resou…
ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search
Shuai Wang, Shengyao Zhuang, Bevan Koopman +1
Federated search, which involves integrating results from multiple independent search engines, will become increasingly pivotal in the context of Retrieval-Augmented Generation pip…
Evaluating Generative Ad Hoc Information Retrieval
Lukas Gienapp, Harrisen Scells, Niklas Deckers +9
Recent advances in large language models have enabled the development of viable generative retrieval systems. Instead of a traditional document ranking, generative retrieval system…
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Shuai Wang, Harrisen Scells, Martin Potthast +2
Screening prioritisation in medical systematic reviews aims to rank the set of documents retrieved by complex Boolean queries. Prioritising the most important documents ensures tha…
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
Shuai Wang, Harrisen Scells, Bevan Koopman +1
Systematic reviews are comprehensive reviews of the literature for a highly focused research question. These reviews are often treated as the highest form of evidence in evidence-b…
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction
Shuai Wang, Hang Li, Guido Zuccon
Boolean query construction is often critical for medical systematic review literature search. To create an effective Boolean query, systematic review researchers typically spend we…