Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding (2024)

Abstract: Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarchical framework that first plans and then writes. Despite the fact that the generated novels reach a sufficient length, they exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. In this paper, we propose a method named Extracting Excelsior and Expanding. Ex3 initially extract structural information by learning from raw novel data. By combining this structure information with the novel data, an instruction-following dataset is meticulously crafted. This dataset is then utilized to fine-tune the LLM, aiming for excelsior generation performance. In the final stage, a tree-like expansion method is deployed to facilitate the generation of arbitrarily long novels.Evaluation against previous methods showcases Ex3’s ability to produce higher-quality long-form novels.

## introduction the realm of automatic long-form novel generation through artificial intelligence holds significant appeal both in academic and practical domains, yet has posed persistent challenges for a long time. this demands not only solid language expression ability @xcite but also strong logical thinking and organizational skills to conceive an appealing and coherent storyline. in recent times, large language models (llms) possess excellent language expression ability and basic common sense reasoning skills, thus leveraging llms for narrative generation is promising. however, generating long novels is still challenging, because of the inherent fixed-size context design in the transformer architecture. some works @xcite @xcite propose the hierarchical generation approach, where general-purpose llms are used to directly generate outlines, and followed by subsequent prompts to generate novels in accordance with the previously devised outlines. despite being able to produce novels that are sufficiently long, these methods have issues with the logical coherence and appeal of the storyline and the deficiencies in character and event depiction, resulting in novels that appear highly unnatural. the reason is that the general-purpose llm has insufficient planning and linguistic capabilities in the field of novel writing, which cannot be solved simply by prompt engineering. we believe that designing ai writing systems should not only consider how people write but also how people learn to write. how do human writers learn to construct reasonable storylines and improve their linguistic capacity? reading is one of the best ways to become a better writer. the more you read and summarize, the better you will write. inspired by this, we propose to learn organization skills and linguistic capacity from human writing. we first organize outlines from existing novels and then conduct the reverse process, that is, creating an outline and developing the outline into a novel. it's like a regular rewriting exercise that writers do. in particular, we propose the extract excelsior and expand framework (ex 3 ) to automatically construct storyline by learning from raw novels, and generate new novels that imitate the linguistic style of a specific genre. ex 3 first extracts structural information from the raw novel data using selfinstructing method. in order to obtain different levels of structure information, we divide the text into sections according to content similarity. additionally, we utilize entity extraction techniques to develop rich profiles of the characters, further enhancing the narrative's depth and coherence. in the excelsior stage, we construct an instructionfollowing dataset with the extracted structure information and the raw novel. then we enhance the llm by finetuning it with the dataset. through this process, the language used in the generated novels more closely aligns with the stylistic choices made by human novelists, resulting in a more natural and engaging reading experience. at last, a tree-like method is used to expand a premise to a novel. we use a depth-first approach to generate the novel, combined with entity extraction to make the content of the novel more coherent. finally, all the leaves of the tree make up the novel. the proposed ex 3 is a self-improvement framework. with the insight that summarizing is easier than expanding, we use the same llm in three stages, that is, the llm summarizes the novel to create the dataset and improve itself, which is similar to how human writers learn. to evaluate the proposed ex 3 for long-term novel generation, we compare its generated novels to similar-length novels generated by several previous methods. we evaluate the novel by both human study and automation metrics. the results demonstrate that ex 3 is able to generate long-term novels with higher quality. ## extract, excelsior, and expand this section introduces the proposed extracting excelsior and expanding (ex 3 ) framework. as figure 1 shows, the framework consists of three ## ablation study ablated modules. in order to better understand the effect of different components in ex 3 on the quality of the generated novels, we set up ablation experiments for different components. the ablation experiment for the setup is shown below: ## conclusion in this paper, we present ex 3 , an automatic novel writing framework. ex 3 first automatically extracts structure information by learning from raw novel data and then creates an instruction-following dataset with this structure information and the novel data. subsequently, this dataset is employed to fine-tune the llm for excelsior generation performance. with the tuned llm, ex 3 employs a tree-like expansion method to generate arbitrarily long novels. the experiment results demonstrate that ex 3 is able to produce higher-quality longform novels than previous methods.