An Analysis of Scoring Methods for Reranking in Large Language Model Story Generation
Bibliographic record
Abstract
Outline-conditioned story generation using Large Language Models (LLMs) offers a promising approach for automating narrative creation.Some outline-conditioned story generation methods use automatic scoring during the generation process in order to improve the story quality.However, current research has shown that automatic scoring is not ideal for assessing story quality.This paper evaluates three proposed automatic story-scoring methods to improve the reranking of outputs during the generation process.These scoring methods leverage different prompting strategies and finetuning techniques to enhance the accuracy and relevance of the assessments.By experimenting with these approaches within a beam search framework, we aim to identify the most effective methods for optimizing story-generation outcomes.While we have found no significant overall difference between these methods in terms of their agreement with human ratings during story generation, the overall story ratings by human evaluators are average.These findings motivate the need for improved automatic scoring techniques and datasets while also indicating that simpler, more easily implementable scoring methods for reranking perform comparably to more complex approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".