Large Language Models (LLMs) exhibit persistent biases reminiscent of historical predictive algorithms, impacting their ability to recall long-tail knowledge. Despite attempts to mitigate these biases through methods like upsampling underrepresented features and employing shapely values for data evaluation, significant challenges remain due to limited mechanistic interpretability in LLMs. Furthermore, biases extend beyond overt categories to include subtler biases related to the language and content of the outputs, raising concerns about representation and diversity in AI-generated text.
Recent studies highlight that LLMs suffer from biases similar to historical machine learning algorithms, particularly in recalling underrepresented or long-tail knowledge.
While various methods seek to ameliorate bias in LLMs, understanding remains limited, emphasizing the need for continued research in mechanistic interpretability.
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