Deceiving to Enlighten: Coaxing LLMs to Self-Reflection for Enhanced Bias Detection and Mitigation

Large Language Models (LLMs) embed complex biases and stereotypes that can lead to detrimental user experiences and societal consequences, often without conscious awareness from the models themselves. This paper emphasizes the importance of equipping LLMs with mechanisms for better self-reflection and bias recognition. Our experiments demonstrate that by informing LLMs that their generated content does not represent their own views and questioning them about bias, their capability to identify and address biases improves. This enhancement is attributed to the internal attention mechanisms and potential internal sensitivity policies of LLMs. Building upon these findings, we propose a novel method to diminish bias in LLM outputs. This involves engaging LLMs in multi-role scenarios acting as different roles where they are tasked for bias exposure, with a role of an impartial referee in the end of each loop of debate. A ranking scoring mechanism is employed to quantify bias levels, enabling more refined reflections and superior output quality. Comparative experimental results confirm that our method outperforms existing approaches in reducing bias, making it a valuable contribution to efforts towards more ethical AI systems.

Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval

This paper studies the task of best counter-argument retrieval given an input argument. Following the definition that the best counter-argument addresses the same aspects as the input argument while having the opposite stance, we aim to develop an efficient and effective model for scoring counter-arguments based on similarity and dissimilarity metrics. We first conduct an experimental study on the effectiveness of available scoring methods, including traditional Learning-To-Rank (LTR) and recent neural scoring models. We then propose Bipolar-encoder, a novel BERT-based model to learn an optimal representation for simultaneous similarity and dissimilarity. Experimental results show that our proposed method can achieve the accuracy@1 of 49.04\%, which significantly outperforms other baselines by a large margin. When combined with an appropriate caching technique, Bipolar-encoder is comparably efficient at prediction time.