2 papers
cs.AI2026
Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction
João Sedoc, Baotong Zhang, Dean Foster
Reliably knowing when a language model is correct is almost as important as being correct. We introduce prover-verifier deliberation (PVD), an inference-time protocol grounded in i…
cs.LG2026
Optimal Budgeted Adaptation of Large Language Models
Jing Wang, Jie Shen, Dean Foster +2
The trade-off between labeled data availability and downstream accuracy remains a central challenge in fine-tuning large language models (LLMs). We propose a principled framework f…