28 citations · 29 across the 5 of their papers we have counts for
5 papers
Penalizing Length: Uncovering Systematic Bias in Quality Estimation Metrics
Yilin Zhang, Wenda Xu, Zhongtao Liu +2
Quality Estimation (QE) metrics are vital in machine translation for reference-free evaluation and increasingly serve as selection criteria in data filtering and candidate rerankin…
Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data
Zhongtao Liu, Parker Riley, Daniel Deutsch +4
Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. T…
On the Implications of Verbose LLM Outputs: A Case Study in Translation Evaluation
Eleftheria Briakou, Zhongtao Liu, Colin Cherry +1
This paper investigates the impact of verbose LLM translations on evaluation. We first demonstrate the prevalence of this behavior across several LLM outputs drawn from the WMT 202…
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Biao Zhang, Zhongtao Liu, Colin Cherry +1
While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scalin…
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback
Wenda Xu, Daniel Deutsch, Mara Finkelstein +6
Recent large language models (LLM) are leveraging human feedback to improve their generation quality. However, human feedback is costly to obtain, especially during inference. In t…