Papers by Junyu Mao
MELA: Multilingual Evaluation of Linguistic Acceptability (2024.acl-long)
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| Challenge: | Existing benchmarks on linguistic acceptability have been used to evaluate language models' ability to distinguish between acceptable and unacceptable sentences. |
| Approach: | They present the largest benchmark to date on linguistic acceptability: MELA . they establish LLM baselines on this benchmark and investigate cross-lingual transfer in acceptability judgements with XLM-R. |
| Outcome: | The proposed model outperforms open-source models on cross-lingual transfer in acceptability judgements. |
Do Prompt Positions Really Matter? (2024.findings-naacl)
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| Challenge: | Prompt-based learning models have a high level of interest due to their ability to perform zero-shot and fewshot tasks. |
| Approach: | They conduct the most comprehensive analysis to date of prompt position for diverse natural language processing tasks. |
| Outcome: | The proposed model is more robust than previous models and is consistent even in instruction-tuned models. |
Efficient Data Labeling by Hierarchical Crowdsourcing with Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) have been gaining attention for their impressive performance in in-context dialogues. |
| Approach: | They propose a hierarchical framework that leverages multiple LLMs for efficient data labeling under budget constraints. |
| Outcome: | The proposed framework outperforms human labelers and GPT-4 in terms of accuracy and efficiency. |