26 citations · 150 across the 47 of their papers we have counts for
8 papers · 1 filter
Prompting Strategy Effects Depend on the Judge: A Multilingual Study of LLM-as-a-Judge Robustness
Asal Mehrabi, Mohammad Aliannejadi, Clemencia Siro
LLM-as-a-Judge evaluation is increasingly applied to multilingual tasks, yet prompting strategies such as Chain-of-Thought and Role Conditioning are recommended based solely on Eng…
The Powerless Noise: How Experimental Settings Shape the Reported Power of Noise
Michał Mazuryk, Fleur Dolmans, Louis Gehringer +3
Recent work has suggested that adding irrelevant documents to the input of retrieval-augmented generation (RAG) systems can improve question-answering performance, a phenomenon ref…
Improving the Efficiency and Effectiveness of LLM Knowledge Distillation for Conversational Search
Stan Fris, Jan Hutter, Jan Henrik Bertrand +2
Conversational Search (CS) considers retrieval of relevant documents based on conversational context. Large Language Models (LLMs) have significantly enhanced CS by enabling effect…
Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval
Arne Eichholtz, Yongkang Li, Jutte Vijverberg +2
The Hypencoder, proposed by Killingback et al., is a retrieval framework that replaces the fixed inner-product scoring function used in standard bi-encoders with a query-specific n…
Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents
Mahta Rafiee, Heydar Soudani, Zahra Abbasiantaeb +3
Deep research agents have emerged as LLM-based systems designed to perform multi-step information seeking and reasoning over large, open-domain sources to answer complex questions…
Do Images Clarify? A Study on the Effect of Images on Clarifying Questions in Conversational Search
Clemencia Siro, Zahra Abbasiantaeb, Yifei Yuan +2
Conversational search systems increasingly employ clarifying questions to refine user queries and improve the search experience. Previous studies have demonstrated the usefulness o…
Learning to Judge: LLMs Designing and Applying Evaluation Rubrics
Clemencia Siro, Pourya Aliannejadi, Mohammad Aliannejadi
Large language models (LLMs) are increasingly used as evaluators for natural language generation, applying human-defined rubrics to assess system outputs. However, human rubrics ar…
Re-Rankers as Relevance Judges
Chuan Meng, Jiqun Liu, Mohammad Aliannejadi +3
Using large language models (LLMs) to predict relevance judgments has shown promising results. Most studies treat this task as a distinct research line, e.g., focusing on prompt de…