8 papers
From Noise to Order: Learning to Rank via Denoising Diffusion
Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh +4
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the…
ADORE: Iterative Query Expansion with Retrieval-Grounded Relevance Feedback
Amin Bigdeli, Negar Arabzadeh, Radin Hamidi Rad +3
LLM-based query expansion improves retrieval by enriching the original query with additional context. Yet most methods remain generation-driven, producing plausible pseudo-document…
The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence
Kelly McConvey, Jalehsadat Mahdavimoghaddam, Nima Jamali +8
The growing ability of generative models to produce realistic documents poses a direct challenge to evidentiary workflows in the justice system and the courts, where decisions incr…
DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities
Sajad Ebrahimi, Nima Jamali, Bardia Shirsalimian +8
The growing popularity and capacity of generative models have eroded the distinction between human and machine-generated content, motivating a growing body of work on detection acr…
PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality
Sajad Ebrahimi, Soroush Sadeghian, Ali Ghorbanpour +6
The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools to assess review quality. We…
Peerispect: Claim Verification in Scientific Peer Reviews
Ali Ghorbanpour, Soroush Sadeghian, Alireza Daghighfarsoodeh +4
Peer review is central to scientific publishing, yet reviewers frequently include claims that are subjective, rhetorical, or misaligned with the submitted work. Assessing whether r…