Researchers have developed FORGE, a novel framework that simultaneously optimizes language model prompts and synthesizes new training data. This approach addresses the limitation of existing methods that only revise prompts while keeping training data fixed, which can lead to unexplored failure conditions. By treating each failure as a signal for both prompt revision and data synthesis, FORGE generates new training instances through four mutation strategies. Tested across eight benchmarks, FORGE demonstrated a significant improvement in aggregate scores and showed positive transfer effects in subsequent studies. AI
IMPACT This research could lead to more efficient and effective methods for training and fine-tuning large language models, potentially improving their performance on a wider range of tasks.
RANK_REASON The cluster contains a research paper detailing a new framework for improving language models. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- CORE Recommender
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- ScienceCast
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