4 papers · 1 filter
Shrinking the Generation-Verification Gap with Weak Verifiers
Jon Saad-Falcon, E. Kelly Buchanan, Mayee F. Chen +9
Verifiers can improve language model capabilities by scoring and ranking responses from generated candidates. Currently, high-quality verifiers are either unscalable (e.g., humans)…
When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework
Zhen Xu, Shang Zhu, Jue Wang +5
We investigate the challenge of applying Large Language Models (LLMs) to long texts. We propose a theoretical framework that distinguishes the failure modes of long context tasks i…
Improving Model Alignment Through Collective Intelligence of Open-Source LLMS
Junlin Wang, Roy Xie, Shang Zhu +6
Building helpful and harmless large language models (LLMs) requires effective model alignment approach based on human instructions and feedback, which necessitates high-quality hum…
Scaling Instruction-Tuned LLMs to Million-Token Contexts via Hierarchical Synthetic Data Generation
Linda He, Jue Wang, Maurice Weber +3
Large Language Models (LLMs) struggle with long-context reasoning, not only due to the quadratic scaling of computational complexity with sequence length but also because of the sc…