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.
Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability
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Early-token log-probabilities from LLM decoding are stronger predictors of reasoning quality than full-sequence statistics in multi-agent debate on essay scoring tasks.
In two-agent debate, log-probability confidence aligns with LLM-judged reasoning quality roughly twice as strongly for the Constructor (AUROC 0.804 for critical failure detection) as for the Auditor (0.634).
citing papers explorer
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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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Early-Token Confidence Predicts Reasoning Quality in Multi-Agent LLM Debate
Early-token log-probabilities from LLM decoding are stronger predictors of reasoning quality than full-sequence statistics in multi-agent debate on essay scoring tasks.
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The Confident Liar: Diagnosing Multi-Agent Debate with Log-Probabilities and LLM-as-Judge
In two-agent debate, log-probability confidence aligns with LLM-judged reasoning quality roughly twice as strongly for the Constructor (AUROC 0.804 for critical failure detection) as for the Auditor (0.634).