21 citations · 48 across the 24 of their papers we have counts for
8 papers · 1 filter
Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels
Jianhao Yan, Pingchuan Yan, Yulong Chen +3
This study presents a comprehensive evaluation of GPT-4's translation capabilities compared to human translators of varying expertise levels. Through systematic human evaluation us…
ELICIT: LLM Augmentation via External In-Context Capability
Futing Wang, Jianhao Yan, Yue Zhang +1
Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application. Traditional fine-tuning methods require substantial data and co…
Keys to Robust Edits: from Theoretical Insights to Practical Advances
Jianhao Yan, Futing Wang, Yun Luo +2
Large language models (LLMs) struggle with maintaining accurate knowledge due to conflicting/outdated parametric memories. While locate-and-edit methods address this, their relianc…
See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses
Yulong Chen, Yang Liu, Jianhao Yan +6
The impressive performance of Large Language Models (LLMs) has consistently surpassed numerous human-designed benchmarks, presenting new challenges in assessing the shortcomings of…
GPT-4 vs. Human Translators: A Comprehensive Evaluation of Translation Quality Across Languages, Domains, and Expertise Levels
Jianhao Yan, Pingchuan Yan, Yulong Chen +3
This study comprehensively evaluates the translation quality of Large Language Models (LLMs), specifically GPT-4, against human translators of varying expertise levels across multi…
What Have We Achieved on Non-autoregressive Translation?
Yafu Li, Huajian Zhang, Jianhao Yan +2
Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate wi…
Potential and Challenges of Model Editing for Social Debiasing
Jianhao Yan, Futing Wang, Yafu Li +1
Large language models (LLMs) trained on vast corpora suffer from inevitable stereotype biases. Mitigating these biases with fine-tuning could be both costly and data-hungry. Model…
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
Jianhao Yan, Yun Luo, Yue Zhang
The application scope of large language models (LLMs) is increasingly expanding. In practical use, users might provide feedback based on the model's output, hoping for a responsive…