R$^2$: A LLM Based Novel-to-Screenplay Generation Framework with Causal Plot Graphs
This addresses a domain-specific need for the TV, film, or opera industries to reduce adaptation costs, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the problem of automatically adapting novels into screenplays by proposing the R^2 framework, which uses large language models (LLMs) with hallucination-aware refinement and causal plot graphs, resulting in substantial performance gains over existing approaches (e.g., 51.3%, 22.6%, and 57.1% absolute increases in win rates).
Automatically adapting novels into screenplays is important for the TV, film, or opera industries to promote products with low costs. The strong performances of large language models (LLMs) in long-text generation call us to propose a LLM based framework Reader-Rewriter (R$^2$) for this task. However, there are two fundamental challenges here. First, the LLM hallucinations may cause inconsistent plot extraction and screenplay generation. Second, the causality-embedded plot lines should be effectively extracted for coherent rewriting. Therefore, two corresponding tactics are proposed: 1) A hallucination-aware refinement method (HAR) to iteratively discover and eliminate the affections of hallucinations; and 2) a causal plot-graph construction method (CPC) based on a greedy cycle-breaking algorithm to efficiently construct plot lines with event causalities. Recruiting those efficient techniques, R$^2$ utilizes two modules to mimic the human screenplay rewriting process: The Reader module adopts a sliding window and CPC to build the causal plot graphs, while the Rewriter module generates first the scene outlines based on the graphs and then the screenplays. HAR is integrated into both modules for accurate inferences of LLMs. Experimental results demonstrate the superiority of R$^2$, which substantially outperforms three existing approaches (51.3%, 22.6%, and 57.1% absolute increases) in pairwise comparison at the overall win rate for GPT-4o.