17 papers
ZenGen: Social Mind for LLMs
ZenGen Team, Zing Team, Ao Xiang +57
As large language models move from isolated task solving toward long-term service in human environments, they require social intelligence: the ability to infer mental states, track…
Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation
Yunfei Zhong, Jun Yang, Wei Huang +7
Deployable multilingual rerankers must generalize across languages, domains, and target ranking tasks while remaining efficient enough for second-stage reranking. However, adapting…
On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
Yongkang Li, Panagiotis Eustratiadis, Yixing Fan +1
Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substantial performance gains and broad…
AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning
Hongru Song, Yu-An Liu, Ruqing Zhang +4
Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack surfaces. We study knowledge-base…
Reason to Retrieve: Enhancing Query Understanding through Decomposition and Interpretation
Yunfei Zhong, Jun Yang, Yixing Fan +4
Query understanding (QU) aims to accurately infer user intent to improve document retrieval. It plays a vital role in modern search engines. While large language models (LLMs) have…
Does Generative Retrieval Overcome the Limitations of Dense Retrieval?
Yingchen Zhang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval (GR) has emerged as a new paradigm in neural information retrieval, offering an alternative to dense retrieval (DR) by directly generating identifiers of relev…