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20232026
most citedAGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

62 citations · 72 across the 13 of their papers we have counts for

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Showing 2023 · cs.CLShow all

6 papers · 2 filters

cs.CL2023

PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion

Yiduo Guo, Zekai Zhang, Yaobo Liang +2

Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translat…

cs.CL2023★ 2 cited

EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation

Wang You, Wenshan Wu, Yaobo Liang +8

Plan-and-Write is a common hierarchical approach in long-form narrative text generation, which first creates a plan to guide the narrative writing. Following this approach, several…

cs.CL2023

Class-Incremental Learning based on Label Generation

Yijia Shao, Yiduo Guo, Dongyan Zhao +1

Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) se…

cs.CL2023

Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast

Yiduo Guo, Yaobo Liang, Dongyan Zhao +2

Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, ev…

cs.CL2023★ 2 cited

Learning to Plan with Natural Language

Yiduo Guo, Yaobo Liang, Chenfei Wu +3

Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide…

cs.CL2023★ 62 cited

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Wanjun Zhong, Ruixiang Cui, Yiduo Guo +6

Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intell…