1 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs?
Adithya Bhaskar, Alexander Wettig, Tianyu Gao +2
Language models handle increasingly long contexts for tasks such as book summarization, but this leads to growing memory costs for the key-value (KV) cache. Many prior works have p…
LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation
Xi Ye, Fangcong Yin, Yinghui He +5
Existing benchmarks for evaluating long-context language models (LCLMs) primarily focus on long-context recall, requiring models to produce short responses based on a few critical…
Metadata Conditioning Accelerates Language Model Pre-training
Tianyu Gao, Alexander Wettig, Luxi He +3
The vast diversity of styles, domains, and quality levels present in language model pre-training corpora is essential in developing general model capabilities, but efficiently lear…
HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
Howard Yen, Tianyu Gao, Minmin Hou +5
Many benchmarks exist for evaluating long-context language models (LCLMs), yet developers often rely on synthetic tasks such as needle-in-a-haystack (NIAH) or an arbitrary subset o…
How to Train Long-Context Language Models (Effectively)
Tianyu Gao, Alexander Wettig, Howard Yen +1
We study continued training and supervised fine-tuning (SFT) of a language model (LM) to make effective use of long-context information. We first establish a reliable evaluation pr…