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This paper tests whether a large language model's sentiment labels on literary text can train smaller BERT models, which would reduce reliance on scarce human annotation. The smaller models come close to the teacher on rank correlation, and BERT-base beats the teacher on non-neutral sentences. The evaluation rests on one novel and a mostly neutral dataset, where aggregate error can mislead, so the results are promising but not yet general.
Distilling Literary Sentiment from LLM Annotations into an Ordinal BERT Model
arXiv:2611.02985v1 Announce Type: new Abstract: The scarcity of human-annotated literary corpora limits the development of domain-specific sentiment analysis models. This study investigates whether LLM-generated annotations can supervise smaller models for literary sentiment analysis. We fine-tune five BERT models using ordinal regression on 10,889 literary sentences annotated by Gemma 4 31B on a ten-point sentiment scale. The models are evaluated against 1,923 human-annotated sentences from Hemingway's "The Old Man and the Sea", alongside the teacher model and conventional baselines. BERT-large achieves a Kendall's $\tau$ of 0.589, approaching the teacher's 0.604. Although a constant-neutral predictor achieves the lowest overall mean absolute error (MAE), BERT-base outperforms the teacher on non-neutral sentences, achieving an MAE of 0.726 versus 1.116. These findings demonstrate the potential of LLM-generated supervision for developing smaller literary sentiment models, while highlighting the limitations of aggregate error metrics on predominantly neutral datasets.
Abstract:The scarcity of human-annotated literary corpora limits the development of domain-specific sentiment analysis models. This study investigates whether LLM-generated annotations can supervise smaller models for literary sentiment analysis. We fine-tune five BERT models using ordinal regression on 10,889 literary sentences annotated by Gemma 4 31B on a ten-point sentiment scale. The models are evaluated against 1,923 human-annotated sentences from Hemingway's "The Old Man and the Sea", alongside the teacher model and conventional baselines. BERT-large achieves a Kendall's $\tau$ of 0.589, approaching the teacher's 0.604. Although a constant-neutral predictor achieves the lowest overall mean absolute error (MAE), BERT-base outperforms the teacher on non-neutral sentences, achieving an MAE of 0.726 versus 1.116. These findings demonstrate the potential of LLM-generated supervision for developing smaller literary sentiment models, while highlighting the limitations of aggregate error metrics on predominantly neutral datasets.
Computation and Language (cs.CL)
arXiv:2611.02985 [cs.CL]
(or arXiv:2611.02985v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.02985 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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