The Spinning the Golden Thread (SGT) benchmark can be expanded and adapted to capture additional aspects of long-form text generation through the following approaches:
Coherence Evaluation Metrics: Introducing specific metrics to assess coherence, such as discourse coherence and logical flow, can provide insights into how well the generated text maintains a consistent narrative or argument throughout its length. This could involve using automated coherence scoring systems or human evaluations focused on narrative structure.
Creativity Assessment: To evaluate creativity, the benchmark could incorporate tasks that require innovative thinking or unique content generation. This could involve prompts that encourage the model to produce original stories, poems, or design concepts, with evaluation criteria focused on novelty and imaginative elements.
Task-Specific Quality Metrics: Developing tailored evaluation metrics for different scenarios within the SGT benchmark can help assess the quality of content specific to each task. For instance, in urban planning tasks, metrics could focus on feasibility and adherence to planning principles, while in creative writing tasks, metrics could assess character development and thematic depth.
User-Centric Feedback: Incorporating user feedback mechanisms into the evaluation process can provide valuable insights into the perceived quality of generated content. This could involve gathering qualitative assessments from users regarding the relevance, engagement, and satisfaction with the generated outputs.
Diverse Task Scenarios: Expanding the range of scenarios within the SGT benchmark to include various genres and formats (e.g., technical writing, narrative storytelling, persuasive essays) can help capture a broader spectrum of long-form generation capabilities. Each scenario can be designed with specific evaluation criteria that reflect the unique demands of that genre.
By implementing these strategies, the SGT benchmark can evolve into a more comprehensive tool for evaluating the multifaceted nature of long-form text generation in long-context LLMs.