overgenerated

Overgenerated is a term used in linguistics and natural language processing to describe outputs that exceed the bounds of acceptable content for a given task. In NLP, overgeneration refers to generation that is irrelevant, self-contradictory, ungrammatical, or factually incorrect, including hallucinated facts or fabricated entities. It can arise from the combination of powerful language models and broad prompts, decoding methods that favor fluency over accuracy, or training data containing inconsistent information.

In computational linguistics, overgeneration describes a grammar, parser, or rule system that accepts strings not permitted by the target language’s syntax, often indicating under-constrained or over-permissive rules. Causes include training data noise, lack of alignment with factual constraints, evaluation metrics that reward variety, and decoding strategies such as beam search that may produce overly diverse but incorrect outputs.

Mitigation strategies include careful data curation, instruction tuning, reinforcement learning from human feedback, adding factual grounding with retrieval or knowledge bases, and post-generation filtering or verification. Evaluation of overgeneration is challenging; metrics may not capture correctness, so human evaluation and targeted factuality checks are commonly used. Overgeneration harms reliability and user trust, especially in domains requiring factual accuracy or safety. Researchers aim to reduce it through better grounding, controllable generation, and robust evaluation. See also: hallucination, misgeneration, linguistic acceptability.