13 citations · 17 across the 10 of their papers we have counts for
10 papers
Low-Latency Real-Time Audio Game Commentary System via LLM-Based Parallel Text Generation
Ryota Kawamatsu, Anum Afzal, Yuki Saito +5
We present a low-latency real-time audio game commentary system that generates spoken commentary directly from live gameplay video. In this end-to-end setting, a key bottleneck is…
Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches
Anum Afzal, Yuki Saito, Hiroya Takamura +5
Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in domains such as sports, esports, and li…
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…
Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion
Anum Afzal, Florian Matthes, Gal Chechik +1
We investigate whether the success of a zero-shot Chain-of-Thought (CoT) process can be predicted before completion. We discover that a probing classifier, based on LLM representat…
JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry
Anum Afzal, Alexandre Mercier, Florian Matthes
Online platforms are increasingly interested in using Data-to-Text technologies to generate content and help their users. Unfortunately, traditional generative methods often fall i…
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…
AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
Anum Afzal, Ribin Chalumattu, Florian Matthes +1
Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that asses their abilities to easily adapt to different do…
Challenges in Domain-Specific Abstractive Summarization and How to Overcome them
Anum Afzal, Juraj Vladika, Daniel Braun +1
Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as d…