The LongForm dataset is created by leveraging English corpus examples with augmented instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization.

We release the LongForm dataset on Github and on HuggingFace. Check out the paper or these links for more detail.

The LongForm Models: We release the models on the model hub of HuggingFace. We cannot release LongForm-LLaMA-7B publicly due to restrictions of LLaMA models.

LongForm-T5-XL: https://huggingface.co/akoksal/LongForm-T5-XL

LongForm-OPT-2.7B: https://huggingface.co/akoksal/LongForm-OPT-2.7B

LongForm-OPT-6.7B: https://huggingface.co/akoksal/LongForm-OPT-6.7B

We provide in-depth evaluation of LongForm models and baselines in the paper. We present the METEOR scores of models in out-of-domain datasets. In all tasks, Recipe Generation (RGen), long-form question answering (ELI5), short story generation (WritingPrompts/WP), LongForm models outperform prior instruction-tuned models.

The LongForm dataset and models mainly focus on long text generation and have limitations regarding structured prediction tasks in NLP. Additionally, we observe that LongForm models may present hallucination problems similar to those found in LLMs.