PwS poisons CLLMs via a two-step training process so that code-style triggers cause vulnerable outputs while normal performance on benchmarks remains largely intact.
Building Language Models for Text with Named Entities
2 Pith papers cite this work, alongside 38 external citations. Polarity classification is still indexing.
2
Pith papers citing it
38
external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.
citing papers explorer
-
Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.