Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drift, amplification of harmful content, and the propagation of unwanted patterns during extended dialogue and conversations. Although training-time or data-centric methods attempt to reduce these effects, they are computationally expensive, irreversible once deployed, and slow to adapt to new conversational contexts. Pruning-based methods provide a flexible and transparent way to reduce bias by adjusting the neurons responsible for certain behaviors. However, most existing approaches are static; once a neuron is removed, the model loses the ability to adapt when the conversation or context changes. To address this, we propose a dynamic, reversible, pruning-based framework that detects context-aware neuron activations and applies adaptive masking to modulate their influence during generation. Our inference-time solution provides fine-grained, memory-aware mitigation with knowledge-preserved, more coherent behavior across multilingual single- and multi-turn dialogues, enabling dynamic fairness control in real-world conversational AI.

Large Language Models (LLMs) increasingly shape public discourse, yet most evaluations of economic and political bias have focused on high-resource Western languages and contexts. This leaves a blind spots in low-resource, multilingual regions such as Pakistan, where linguistic identity is closely tied to regional, religious, and political ideologies. We present a systematic evaluation of political bias in 13 state-of-the-art LLMs across five Pakistani languages: Urdu, Punjabi, Sindhi, Pashto, and Balochi. Our framework integrates a culturally adapted Political Compass Test (PCT) with multi-level framing analysis, capturing both ideological stance (economic/social axes) and stylistic framing (content, tone, emphasis). The prompts are aligned with 11 socio-political themes specific to the Pakistani context. The results show that while LLMs significantly reflect liberal-left orientations consistent with Western training data, they exhibit more authoritarian framing in regional languages, highlighting language-conditioned ideological modulation. We also identify model-specific bias patterns in all languages. These findings show the need for culturally grounded multilingual bias examining frameworks in NLP. Code and dataset are available.

Recent advancements in large language models (LLMs) have enabled their widespread use across diverse real-world applications. However, concerns remain about their tendency to encode and reproduce ideological biases along political and economic dimensions. In this paper, we employ a framework for probing and mitigating such biases in decoder-based LLMs through analysis of internal model representations. Grounded in the Political Compass Test (PCT), this method uses contrastive pairs to extract and compare hidden layer activations from models like Mistral and DeepSeek. We introduce a comprehensive activation extraction pipeline capable of layer-wise analysis across multiple ideological axes, revealing meaningful disparities linked to political framing. Our results show that decoder LLMs systematically encode representational bias across layers, which can be leveraged for effective steering vector-based mitigation. This work provides new insights into how political bias is encoded in LLMs and offers a principled approach to debiasing beyond surface-level output interventions.

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their reliability and alignment with human expectations remain unresolved challenges. This tutorial introduces the foundations of alignment and provides participants with a conceptual and practical understanding of the field. Core principles such as values, safety, reasoning, and pluralism will be presented through intuitive explanations, worked examples, and case studies. The aim is to equip attendees with the ability to reason about alignment goals, understand how existing methods operate in practice, and critically evaluate their strengths and limitations.