Large language models like those in the GPT suite have no concept of facts or relations; they simply follow a prompt with the statistically most likely continuation. This leads to contradictions and a loss of coherence as the model can’t detect when a story wouldn’t make sense to a human reader. In addition, large language models have no “memory” of the story outside the current context window, leading to even more contradictions and incoherence as later events in the story fail to relate to early ones.
To improve the coherence of large language model generated stories, our system uses a knowledge graph. A knowledge graph is a concise means of representing relationships between and facts about entities. By using a knowledge graph, we can detect and prevent contradictions and the introduction of unrelated entities. Using a knowledge graph also effectively expands the language model’s context window, as the entire knowledge graph, rather than just the limited prompt, can be used for scoring.
Our story generation system uses GPT-Neo paired with a custom-built knowledge graph generation system. GPT-Neo is used to generate candidate continuations for the story, and the knowledge graph is used to score and select the best candidate.
Our knowledge graph generator first uses spaCy to extract entities, disambiguate pronouns, and find semantics-agnostic relations. Then it uses that information to generate more verb-specific relations with VerbNet.
Our system generates candidate continuations using GPT-Neo. We experimented with two methods of generating a prompt:
- Simply use the last
ntokens of the story, - The second method was to use the knowledge graph to form the first
jtokens of the prompt and the story to form the lastn-jtokens.
We developed four distinct knowledge-graph-based scoring functions to compare to a default of scoring all candidates equally well.
| name | description |
|---|---|
| kg_scoring_fn | Our first scoring function is designed to reward reusing entities from the knowledge graph and penalize adding new entities and introducing contradictions. The score is calculated by simply subtracting the number of new entities and contradictions from the number of reused entities. Sentences that are exact repetitions of each other are also given an extremely low score, though in practice that did little to prevent repetitive stories as sentences with slight differences fail to be penalized. |
| kg_scoring_fn2 | Our second scoring function is the simplest. It rewards reusing entities by returning the number of entities from the knowledge graph that are also mentioned in the candidate. |
| kg_scoring_fn3 | Our third scoring function rewards reusing entities and penalizes adding new entities by calculating an F1 score. Entities in both the knowledge graph and the candidate are considered true positives, and entities in one but not the other are false positives (if they are in the candidate only) and false negatives (if they are in the knowledge graph only). |
| kg_scoring_fn4 | Our third scoring function rewards reusing entities and penalizes adding new entities by calculating the BLEU score between the entities in the knowledge graph and the entities in the candidate. |
Developed by students at Georgia Institute of Technology:
- Barun Das bdas31@gatech.edu
- Lasya Venneti svenneti3@gatech.edu
- Kaylah Facey