Lexical Bias

Lexical bias, the disproportionate influence of specific words or phrases on model performance and output, is a significant concern in natural language processing (NLP). Current research focuses on identifying and mitigating this bias across various NLP tasks, including machine translation, toxic language detection, and debate evaluation, employing techniques like counterfactual causal inference and hierarchical Bayesian models to disentangle useful and misleading lexical effects. Understanding and addressing lexical bias is crucial for developing fairer, more accurate, and generalizable NLP systems, with implications for applications ranging from healthcare to social media analysis.