HELMET: Holistically Evaluating Long-Context Language Models
A general overview of HELMET (Holistically Evaluating Long-Context Language Models), a new benchmark created to evaluate language models with extended context windows in a thorough manner
A lot of datasets are only available in contexts with fewer than 32K tokens, which is insufficient for assessing frontier Long-context Language Models
To overcome the shortcomings of current approaches, a new benchmark called HELMET is suggested
A greater variety of jobs with inherently lengthy contexts that reflect real-world applications are included in HELMET
In order to offer an even more complete assessment suite, they are attempting to include LongProc into HELMET, especially for tasks that call for extremely lengthy outputs
HELMET offers a more comprehensive and accurate understanding of LCLM capabilities by overcoming the drawbacks of previous benchmarks with its varied tasks, configurable length