3 citations · 12 across the 30 of their papers we have counts for
32 papers
Detecting Data Contamination in Large Language Models
Juliusz Janicki, Savvas Chamezopoulos, Evangelos Kanoulas +1
Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources. Membership Inference Attacks (MIA) aim to detect tho…
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…
Spectral Tempering for Embedding Compression in Dense Passage Retrieval
Yongkang Li, Panagiotis Eustratiadis, Evangelos Kanoulas
Dimensionality reduction is critical for deploying dense retrieval systems at scale, yet mainstream post-hoc methods face a fundamental trade-off: principal component analysis (PCA…
SubSearch: Intermediate Rewards for Unsupervised Guided Reasoning in Complex Retrieval
Roxana Petcu, Evangelos Kanoulas, Maarten de Rijke
Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often require multi-step reasoning o…
A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech
Jia-Hong Huang, Seulgi Kim, Yi Chieh Liu +5
Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual shift in perceived speaker id…
LLM-based Listwise Reranking under the Effect of Positional Bias
Jingfen Qiao, Jin Huang, Xinyu Ma +4
LLM-based listwise passage reranking has attracted attention for its effectiveness in ranking candidate passages. However, these models suffer from positional bias, where passages…