Code LLMs generate substantially worse comments outside English, and no tested automatic metric or LLM judge reliably matches human assessment of those outputs.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
MemAudit combines counterfactual causal influence scores with memory consistency graphs to identify poisoned records in LLM agent memory, reducing MINJA attack success from 70% to 0% in QA and 83.3% to 0% in reasoning tasks.
HAMR combines meta-learning with hardness-aware weighting and neighborhood resampling to improve minority-class performance on imbalanced NLP datasets.
citing papers explorer
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Evaluating Non-English Developer Support in Machine Learning for Software Engineering
Code LLMs generate substantially worse comments outside English, and no tested automatic metric or LLM judge reliably matches human assessment of those outputs.
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MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection
MemAudit combines counterfactual causal influence scores with memory consistency graphs to identify poisoned records in LLM agent memory, reducing MINJA attack success from 70% to 0% in QA and 83.3% to 0% in reasoning tasks.
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Model-Agnostic Meta Learning for Class Imbalance Adaptation
HAMR combines meta-learning with hardness-aware weighting and neighborhood resampling to improve minority-class performance on imbalanced NLP datasets.