3 citations · 5 across the 5 of their papers we have counts for
5 papers
FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
Anum Afzal, Juraj Vladika, Florian Matthes
Large Language Models tend to struggle when dealing with specialized domains. While all aspects of evaluation hold importance, factuality is the most critical one. Similarly, relia…
Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization
Anum Afzal, Mehul Kumawat, Florian Matthes
Large Language Models (LLMs), being generic task solvers, are versatile. However, despite the vast amount of data they are trained on, there are speculations about their adaptation…
Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
Anum Afzal, Juraj Vladika, Gentrit Fazlija +2
Given the growing trend of many organizations integrating Retrieval Augmented Generation (RAG) into their operations, we assess RAG on domain-specific data and test state-of-the-ar…
Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human in the Loop
Anum Afzal, Alexander Kowsik, Rajna Fani +1
Large Language Models have found application in various mundane and repetitive tasks including Human Resource (HR) support. We worked with the domain experts of SAP SE to develop a…
Investigating Conversational Search Behavior For Domain Exploration
Phillip Schneider, Anum Afzal, Juraj Vladika +2
Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search age…