A generative preference-learning method trains a latent demonstration selector from LLM feedback and improves few-shot in-context learning performance on most of 19 benchmark datasets.
Semantic Noise Matters for Neural Natural Language Generation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97%, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Learning to Select In-Context Demonstration Preferred by Large Language Model
A generative preference-learning method trains a latent demonstration selector from LLM feedback and improves few-shot in-context learning performance on most of 19 benchmark datasets.