arXiv (All)
open access
COFFEQA: Counterfactual-Guided Few-Shot Distillation for LLM Question Answering
Knowledge distillation aims to train a smaller student model to mimic a larger teacher model for resource-constrained environments. However, the process of distillation is typically data-intensive and computationally expensive, particularly for generative tasks. Few-shot distillation offers an alter
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation#819312
- HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation#809319
- Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG#925810
- WordPolo: Evaluating Language Models Through Iterative Semantic Feedback#1013219
- Flux-OPD: On-Policy Distillation with Evolving Contexts#1327472
Record · ID 1296844
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive