Title: Which Cultural Lens Do Models Adopt? On Cultural Positioning Bias and Agentic Mitigation in LLMs
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Bias, Culture, LLM, Generation, Agent

Abstract: Large language models (LLMs) have unlocked a wide range of downstream generative applications. 
However, we found that they also risk perpetuating subtle fairness issues tied to culture, positioning their generations from the perspectives of the mainstream US culture while demonstrating salient externality towards non-mainstream ones.
In this work, we identify and systematically investigate this novel **culture positioning bias**, in which an LLM’s default generative stance aligns with a mainstream view and treats other cultures as "outsiders".
We propose the ***CultureLens*** benchmark with 4,000 generation prompts and 3 evaluation metrics for quantifying this bias through the lens of a *culturally situated interview script generation* task, in which an LLM is positioned as an on-site reporter interviewing local people across 10 diverse cultures. 
Empirical evaluation on 5 state-of-the-art LLMs reveals a stark pattern: while models adopt insider tones in over 88\% US-contexted scripts on average, they disproportionately adopt mainly outsider stances for less dominant cultures.
To resolve these biases, we propose *2 inference-time mitigation methods*: a baseline prompt-based **Fairness Intervention Pillars (FIP)** method, and a structured **Mitigation via Fairness Agents (MFA)** framework consisting of 2 pipelines:
(1) **MFA-SA (Single-Agent)** introduces a self-reflection and rewriting loop based on fairness guidelines.
(2) **MFA-MA (Multi-Agent)** structures the process into a hierarchy of specialized agents: a Planner Agent(initial script generation), a Critique Agent (evaluates initial script against fairness pillars), and a Refinement Agent (incorporates feedback to produce a polished, unbiased script).
Empirical results demonstrate that agent-based MFA methods achieve outstanding and robust performance in mitigating the culture positioning bias: 
For instance, on the CAG metric, *MFA-SA reduces bias in Llama model by 89.70 \% and MFA-MA mitigates bias in Qwen by 82.55\%*.
These findings showcase the effectiveness of agent-based methods as a promising direction for mitigating biases in generative LLMs.