3 citations · 5 across the 9 of their papers we have counts for
9 papers
uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking
Simon Lupart, Kidist Amde Mekonnen, Zahra Abbasiantaeb +1
This report describes our participation in SemEval-2026 Task 8 on multi-turn retrieval and question answering. The task evaluates conversational systems across four domains (financ…
The Multilingual Curse at the Retrieval Layer: Evidence from Amharic
Yosef Worku Alemneh, Kidist Amde Mekonnen, Maarten de Rijke
Multilingual retrieval increasingly underpins cross-lingual question answering and retrieval-augmented generation. Strong zero-shot scores on multilingual benchmarks are often take…
Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval
Kidist Amde Mekonnen, Yongkang Li, Yubao Tang +2
Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to…
A Parametric Memory Head for Continual Generative Retrieval
Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke
Generative information retrieval (GenIR) consolidates retrieval into a single neural model that decodes document identifiers (docids) directly from queries. While this model-as-ind…
Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval
Kidist Amde Mekonnen, Yosef Worku Alemneh, Maarten de Rijke
Neural retrieval methods using transformer-based pre-trained language models have advanced multilingual and cross-lingual retrieval. However, their effectiveness for low-resource,…
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Kidist Amde Mekonnen, Yubao Tang, Maarten de Rijke
Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task, allowing for…
Conditioning GAN Without Training Dataset
Kidist Amde Mekonnen
Deep learning algorithms have a large number of trainable parameters often with sizes of hundreds of thousands or more. Training this algorithm requires a large amount of training…
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling
Kidist Amde Mekonnen, Nicola Dall'Asen, Paolo Rota
Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models, achieving remarkable performance in image synthesis tasks. However, these models f…
Balanced Face Dataset: Guiding StyleGAN to Generate Labeled Synthetic Face Image Dataset for Underrepresented Group
Kidist Amde Mekonnen
For a machine learning model to generalize effectively to unseen data within a particular problem domain, it is well-understood that the data needs to be of sufficient size and rep…