LLM representations encode essay quality in a linearly decodable form that emerges across layers and includes identifiable scoring neurons whose distribution shifts with essay length.
https://arxiv.org/pdf/2008.01441
3 Pith papers cite this work. Polarity classification is still indexing.
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Sequential fine-tuning of LLaMA-3.1-8B on discourse elements in order outperforms independent and randomized curricula for AES on PERSUADE 2.0, with specific F1/accuracy gains and competitiveness vs. LLaMA-70B on conclusion scoring.
MAPLE uses meta-learning with prototypical networks to learn transferable representations and achieves state-of-the-art cross-prompt essay scoring on ELLIPSE, LAILA, and parts of ASAP datasets.
citing papers explorer
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From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models
LLM representations encode essay quality in a linearly decodable form that emerges across layers and includes identifiable scoring neurons whose distribution shifts with essay length.
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The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring
Sequential fine-tuning of LLaMA-3.1-8B on discourse elements in order outperforms independent and randomized curricula for AES on PERSUADE 2.0, with specific F1/accuracy gains and competitiveness vs. LLaMA-70B on conclusion scoring.
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MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring
MAPLE uses meta-learning with prototypical networks to learn transferable representations and achieves state-of-the-art cross-prompt essay scoring on ELLIPSE, LAILA, and parts of ASAP datasets.