Spinning the Golden Thread: Benchmarking Long-Form Generation in
  Language Models
Authors: name: Yuhao Wu, name: Ming Shan Hee, name: Zhiqing Hu, name: Roy Ka-Wei Lee

The abilities of long-context language models (LMs) are often evaluated using
the "Needle-in-a-Haystack" (NIAH) test, which comprises tasks designed to
assess a model's ability to identify specific information ("needle") within
large text sequences ("haystack"). While these benchmarks measure how well
models understand long-context input sequences, they do not effectively gauge
the quality of long-form text generation--a critical aspect for applications
such as design proposals and creative writing. To address this gap, we have
introduced a new long-form text evaluation benchmark, Spinning the Golden
Thread (SGT), which tests models' ability to identify specific events within
generated long text sequences. In this benchmark, we prompt long-context LMs to
create long-form text that must include particular events or constraints and
evaluate their ability to incorporate these elements. We evaluated ten
long-context LMs across four distinct scenarios, three types of prompt
instructions, and two different generation-length settings (16K and 32K).
Although these models perform well on NIAH benchmarks, none demonstrated
satisfactory performance on the Spinning the Golden Thread, raising concerns
about their ability to generate coherent long-form text that follows
instructions. Additionally, as the length of the generated text increases, all
models exhibit a significant drop in performance.