VCM reshapes LLM next-token distributions before truncation via PMI-based context boosts and variance-scaled self-debiasing to reduce repetition and dullness without retraining.
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4 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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Pith papers citing it
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external citations · OpenAlex
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Local LLMs via Ollama match or exceed some local NMT systems and a frontier LLM on a new multilingual corpus but lag behind top commercial NMTs like DeepL.
Outcome-level RL with binary or composite rewards improves compositional generalization over supervised fine-tuning by avoiding overfitting to frequent training patterns.
SemEval-2026 Task 7 presents a benchmark and two evaluation tracks for assessing LLMs on everyday knowledge in diverse languages and cultures without allowing training on the test data.
citing papers explorer
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Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding
VCM reshapes LLM next-token distributions before truncation via PMI-based context boosts and variance-scaled self-debiasing to reduce repetition and dullness without retraining.
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Translation Analytics for Freelancers II: Benchmarking Local LLMs for Confidential Translation Workflows
Local LLMs via Ollama match or exceed some local NMT systems and a frontier LLM on a new multilingual corpus but lag behind top commercial NMTs like DeepL.
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Reinforcement Learning for Compositional Generalization with Outcome-Level Optimization
Outcome-level RL with binary or composite rewards improves compositional generalization over supervised fine-tuning by avoiding overfitting to frequent training patterns.
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SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures
SemEval-2026 Task 7 presents a benchmark and two evaluation tracks for assessing LLMs on everyday knowledge in diverse languages and cultures without allowing training on the test data.