Long-Form Methodical Writing Benchmarking Task
A Long-Form Methodical Writing Benchmarking Task is an NLP benchmarking task that is designed to assess the performance of language models in generating structured, domain-specific, and coherent long-form content that adheres to professional standards and methodologies.
- AKA: Methodical Writing Evaluation Task, Structured Long-Form Generation Benchmark, Domain-Specific Writing Assessment.
- Context:
- Task Input: Detailed prompts outlining task objectives, procedures, and specific inputs pertinent to the domain.
- Optional Input: Supplementary context or background information relevant to the task.
- Task Output: Comprehensive, structured long-form text that aligns with domain-specific conventions and fulfills the outlined objectives.
- Task Performance Measure/Metrics: Evaluated using a combination of reference-based metrics (e.g., BLEU, ROUGE) and reference-less metrics (e.g., human judgment, coherence scores).
- Benchmark datasets (optional): Datasets like DoLoMiTes, which encompass a wide range of expert-authored tasks across various fields.
- It can measure the model’s ability to maintain logical flow, factual consistency, task completion, and domain-specific language fidelity over extended text.
- It can test models for handling complex multi-step reasoning, structured writing constraints, and iterative elaboration required by real-world professional writing tasks.
- It can reveal deficiencies in language models related to long-context memory, structured argumentation, and professional tone maintenance.
- It can involve tasks requiring synthesizing diverse sources, planning multi-section documents, and achieving fine-grained stylistic and procedural adherence.
- It can range from short long-form tasks (500 words) to extended technical or clinical documents (over 3,000 words), depending on domain and task complexity.
- ...
- Example(s):
- DoLoMiTes Benchmarking Task, which evaluates language models on tasks such as drafting clinical reports, educational lesson plans, and technical documentation.
- LongGenBench, which assesses the ability of models to generate long-form content following complex instructions over extended sequences.
- LCFO Benchmark, focusing on summarization and summary expansion capabilities across diverse domains.
- ...
- Counter-Example(s):
- Benchmarks evaluating short-form or generic text generation tasks without domain-specific constraints.
- Creative writing assessments that prioritize imaginative storytelling over structured, factual content.
- General language understanding benchmarks that do not focus on the generation of structured long-form outputs.
- ...
- See: Long-Form Methodical Writing System, Domain-Specific Natural Language Generation Task, Automated Domain-Specific Writing Task.
References
2024a
- (Malaviya et al., 2024) ⇒ C. Malaviya, P. Agrawal, K. Ganchev, P. Srinivasan, F. Huot, J. Berant, M. Yatskar, D. Das, M. Lapata, & C. Alberti. (2024). "Dolomites: Domain-Specific Long-Form Methodical Tasks".
- QUOTE: "Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a teacher writing a lesson plan for students, these tasks are pervasive, requiring to methodically generate structured long-form output for a given input. We develop a typology of methodical tasks structured in the form of a task objective, procedure, input, and output, and introduce DoLoMiTes, a novel benchmark with specifications for 519 such tasks elicited from hundreds of experts from across 25 fields.
2024b
- (Dolomites Benchmark Team, 2024) ⇒ Dolomites Benchmark Team. (2024). "Dolomites: Domain-Specific Long-Form Methodical Tasks".
- QUOTE: "The Dolomites benchmark consists of 519 expert-authored, long-form task descriptions spanning 25 fields, with 1,857 examples that instantiate the tasks with plausible inputs and outputs. Tasks are challenging and require domain expertise.
2024c
- (Wu et al., 2024) ⇒ Yuhao Wu, et al.. (2024). "LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs".
- QUOTE: LongGenBench is a novel benchmark designed to rigorously evaluate large language models' (LLMs) ability to generate long text while adhering to complex instructions.
Through tasks requiring specific events or constraints within generated text, LongGenBench evaluates model performance across four distinct scenarios, three instruction types, and two generation-lengths (16K and 32K tokens).
Our evaluation of ten state-of-the-art LLMs reveals that, despite strong results on Ruler, all models struggled with long text generation on LongGenBench, particularly as text length increased.
- QUOTE: LongGenBench is a novel benchmark designed to rigorously evaluate large language models' (LLMs) ability to generate long text while adhering to complex instructions.
2024c
- (Costa-Jussà et al., 2024) ⇒ Marta R. Costa-Jussà, et al.. (2024). "LCFO: Long Context and Long Form Output Dataset and Benchmarking".
- QUOTE: This paper presents the Long Context and Form Output (LCFO) benchmark, a novel evaluation framework for assessing gradual summarization and summary expansion capability across diverse domains.
LCFO consists of long input documents (5k words average length), each with three summaries of different lengths, as well as approximately 15 question and answer (QA) pairs related to the input content.
The LCFO benchmark offers a standardized platform for evaluating summarization and summary expansion performance, as well as corresponding automatic metrics, thereby providing an important evaluation framework to advance generative AI.
- QUOTE: This paper presents the Long Context and Form Output (LCFO) benchmark, a novel evaluation framework for assessing gradual summarization and summary expansion capability across diverse domains.