3 citations · 6 across the 5 of their papers we have counts for
6 papers
Can Generative Retrievers Learn Semantic IDs Without Forgetting How to Speak?
Junchen Fu, Kleomenis Katevas, Vandana Rajan +2
Generative retrieval (GR) enables end-to-end retrieval by generating document semantic identifiers (SIDs). However, retrieval-only fine-tuning can over-specialize pretrained langua…
AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
Dzung Pham, Kleomenis Katevas, Ali Shahin Shamsabadi +1
Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote,…
Robust Hallucination Detection in LLMs via Adaptive Token Selection
Mengjia Niu, Hamed Haddadi, Guansong Pang
Hallucinations in large language models (LLMs) pose significant safety concerns that impede their broader deployment. Recent research in hallucination detection has demonstrated th…
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
Hongyan Chang, Ali Shahin Shamsabadi, Kleomenis Katevas +2
Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs) aim at determining if a data point was part of the model's training set. Prior MIAs that are built f…
Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
Mengjia Niu, Hao Li, Jie Shi +2
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their…
MELTing point: Mobile Evaluation of Language Transformers
Stefanos Laskaridis, Kleomenis Katevas, Lorenzo Minto +1
Transformers have revolutionized the machine learning landscape, gradually making their way into everyday tasks and equipping our computers with "sparks of intelligence". However,…