2 citations · 4 across the 16 of their papers we have counts for
5 papers · 1 filter
PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics
Daniil Larionov, Steffen Eger
Evaluating the quality of machine-generated natural language content is a challenging task in Natural Language Processing (NLP). Recently, large language models (LLMs) like GPT-4 h…
ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?
Leixin Zhang, Steffen Eger, Yinjie Cheng +6
Multimodal large language models (LLMs) have demonstrated impressive capabilities in generating high-quality images from textual instructions. However, their performance in generat…
PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation
Christoph Leiter, Steffen Eger
Large language models (LLMs) have revolutionized NLP research. Notably, in-context learning enables their use as evaluation metrics for natural language generation, making them par…
xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation Metrics
Daniil Larionov, Mikhail Seleznyov, Vasiliy Viskov +2
State-of-the-art trainable machine translation evaluation metrics like xCOMET achieve high correlation with human judgment but rely on large encoders (up to 10.7B parameters), maki…
Is there really a Citation Age Bias in NLP?
Hoa Nguyen, Steffen Eger
Citations are a key ingredient of scientific research to relate a paper to others published in the community. Recently, it has been noted that there is a citation age bias in the N…