DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
MATRIX embeds multi-layer watermarks in LLM-generated code via dual-channel constrained parity-check encoding, achieving 99.2% detection accuracy with 0-0.14% functionality loss and 7.7-26.67% better attack robustness than prior methods.
LLMs show measurable self-recognition that linearly correlates with self-preference bias in evaluations, supported by fine-tuning experiments and controls for confounders.
citing papers explorer
-
DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis
DSIPA is a zero-shot black-box detector that uses sentiment distribution consistency and preservation metrics to identify LLM text, reporting up to 49.89% F1 gains over baselines across domains and models.
-
MATRIX: Multi-Layer Code Watermarking via Dual-Channel Constrained Parity-Check Encoding
MATRIX embeds multi-layer watermarks in LLM-generated code via dual-channel constrained parity-check encoding, achieving 99.2% detection accuracy with 0-0.14% functionality loss and 7.7-26.67% better attack robustness than prior methods.
-
LLM Evaluators Recognize and Favor Their Own Generations
LLMs show measurable self-recognition that linearly correlates with self-preference bias in evaluations, supported by fine-tuning experiments and controls for confounders.