1 citations · 1 across the 10 of their papers we have counts for
10 papers
Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability
Alyssa Unell, Natalie Dullerud, Naomi Boneh +4
LLM judges are used to reduce the need for costly human labor in evaluating open-ended text generation. However, the reliability of these judges depends critically on their alignme…
Synthetic Data for any Differentiable Target
Tristan Thrush, Sung Min Park, Herman Brunborg +5
What are the limits of controlling language models via synthetic training data? We develop a reinforcement learning (RL) primitive, the Dataset Policy Gradient (DPG), which can pre…
Scaling Self-Play with Self-Guidance
Luke Bailey, Kaiyue Wen, Kefan Dong +2
LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conjecturer model creates problems for a Solver, and both improve together. However, in…
Data-efficient pre-training by scaling synthetic megadocs
Konwoo Kim, Suhas Kotha, Yejin Choi +3
Synthetic data augmentation has emerged as a promising solution when pre-training is constrained by data rather than compute. We study how to design synthetic data algorithms that…
Agentic Adversarial QA for Improving Domain-Specific LLMs
Vincent Grari, Ciprian Tomoiaga, Sylvain Lamprier +2
Large Language Models (LLMs), despite extensive pretraining on broad internet corpora, often struggle to adapt effectively to specialized domains. There is growing interest in fine…
Synthetic bootstrapped pretraining
Zitong Yang, Aonan Zhang, Hong Liu +4
We introduce Synthetic Bootstrapped Pretraining (SBP), a language model (LM) pretraining procedure that first learns a model of relations between documents from the pretraining dat…
Reasoning to Learn from Latent Thoughts
Yangjun Ruan, Neil Band, Chris J. Maddison +1
Compute scaling for language model (LM) pretraining has outpaced the growth of human-written texts, leading to concerns that data will become the bottleneck to LM scaling. To conti…
Synthetic continued pretraining
Zitong Yang, Neil Band, Shuangping Li +2
Pretraining on large-scale, unstructured internet text enables language models to acquire a significant amount of world knowledge. However, this knowledge acquisition is data-ineff…
Linguistic Calibration of Long-Form Generations
Neil Band, Xuechen Li, Tengyu Ma +1
Language models (LMs) may lead their users to make suboptimal downstream decisions when they confidently hallucinate. This issue can be mitigated by having the LM verbally convey t…
Language Models with Conformal Factuality Guarantees
Christopher Mohri, Tatsunori Hashimoto
Guaranteeing the correctness and factuality of language model (LM) outputs is a major open problem. In this work, we propose conformal factuality, a framework that can ensure high…