When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL). However, it remains unclear how different types of NLEs compare in their effects on downstream model performance in explanation-augmented prompting. Therefore, we provide a comparative evaluation across six benchmarks and four instruction-tuned models, studying how NLE source (human-written when available, self-generated explanations, generated by an external LLM) and NLE selection (random vs faithfulness-based filtering) affect d
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- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “When Do Explanations Help In-Context Learning? A Comparative” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Mahdi Dhaini →
“When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulnes”
- LinkedLinked via arxiv author · 85%Adam Dejl →
“When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulnes”
- LinkedLinked via arxiv author · 85%Juraj Vladika →
“When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulnes”
- LinkedLinked via arxiv author · 85%Volkan Özer →
“When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulnes”
- LinkedLinked via arxiv author · 85%Barbara Plank →
“When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulnes”
- LinkedLinked via arxiv author · 85%Gjergji Kasneci →
“When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulnes”
