Can Large Language Models Automatically Score Proficiency of Written Essays? (2024.lrec-main)
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| Challenge: | Automated essay scoring (AES) is one of the earliest research problems in natural language processing. |
| Approach: | They propose to use large language models to analyze and score written essays using four different prompts. |
| Outcome: | The proposed models show comparable performance on four different prompts and a slight advantage over the state-of-the-art models. |
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| Challenge: | Recent advances in automated essay scoring (AES) have relied on labeled essays, requiring tremendous cost and expertise for their acquisition. |
| Approach: | They propose a zero-shot prompting framework that automatically decomposes writing proficiency into distinct traits and generates scoring criteria for each trait. |
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| Challenge: | Existing automated essay scoring relies on essay text without explanatory rationales for the scores. |
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| Challenge: | Recent work on sentence prediction tasks uses shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss. |
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Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
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Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Language Models (LLMs) excel in various tasks, but their evaluation, especially in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. |
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| Challenge: | Existing Automated Essay Scoring (AES) methods focus on sentence-level features, whereas Large Language Models (LLMs) are sensitive to conventions & accuracy, language complexity, and organization. |
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| Challenge: | Recognizing LLMs’ capability to generate educational content can lead to advances in automated and personalized learning. |
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| Challenge: | Using large language models (LLMs) for automatic evaluation has become an important evaluation method in NLP research. |
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Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT (2024.lrec-main)
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Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat, Sepehr Kamahi, Samin Mahdizadeh Sani, Nikoo Naghavian, Danial Namazifard, Pouya Sadeghi, Yadollah Yaghoobzadeh
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EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)
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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
| Challenge: | Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts. |
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ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)
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| Challenge: | Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field. |
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