A new study published on arXiv investigates the effectiveness of natural language explanations (NLEs) in improving in-context learning (ICL) for large language models. The research compares different types of NLEs, including human-written, self-generated, and externally generated explanations, across six benchmarks and four instruction-tuned models. Findings indicate that NLEs generally enhance accuracy in classification tasks, with externally generated explanations often performing comparably to human-written ones. However, the impact on mathematical reasoning tasks is more varied and dependent on the specific model and explanation source. AI
IMPACT Provides insights into optimizing prompt engineering for LLMs by understanding the efficacy of different explanation types.
RANK_REASON The cluster contains a research paper detailing a comparative study on natural language explanations and their impact on in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- CORE Recommender
- DagsHub
- In-Context Learning
- Gotit.pub
- Hugging Face
- Influence Flower
- ScienceCast
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