Current alignment techniques, such as RLHF (Reinforcement Learning from Human Feedback), typically rely on a centralized set of "safety guidelines" that reflect the cultural norms of the model's developers. This often leads to cultural misalignment, where the LLM may inadvertently erase local nuances, apply inappropriate social taboos, or fail to recognize regional linguistic etiquette.
This thesis aims to move beyond "cultural awareness" toward Cultural Verification. The student will design a systematic framework to evaluate and verify whether an LLM’s outputs adhere to the specific socio-cultural norms, value systems, and historical contexts of a target demographic.

Key Responsibilities & Tasks:

The research will be structured around four primary pillars, beginning with the development of a comprehensive Taxonomy of Cultural Correctness to define the specific dimensions—such as social hierarchy, religious sensitivities, and gender roles—necessary for nuanced LLM evaluation. Building upon this foundation, the study will propose a Verification Methodology centered on a "Cultural Guardrail" architecture, utilizing both programmatic checks and human-in-the-loop oversight to validate model responses. To test the efficacy of this framework, a specialized Benchmark will be curated, featuring a localized "red-teaming" suite designed to expose cultural hallucinations and latent insensitivities. Finally, a Comparative Analysis will be conducted to measure the performance of leading models, including GPT-4, Llama 3, and Claude, against these proposed designs across two or more distinct cultural contexts.

The tactile interpretation of images requires the transformation of two-dimensional visual information into three-dimensional representations accessible through touch. This conversion presents significant challenges, particularly in the preservation of crucial expressive elements that in visual reality are highlighted through light and shadow.

The project focuses on case studies of pictorial art; for example, works by Caravaggio, where chiaroscuro creates a true 'tactile map' through:
- Anatomies highlighted by shadows (muscles, ribs, veins)
- Movement-defining fabric folds
- Depth of environments
- Textures of objects (fruit, fabrics, metals)

The objective of this thesis is to develop an AI system that overcomes current limitations in the automatic conversion of images into tactile representations, focusing on:
- Recognition of elements that in visual reality are highlighted by light and shadow
- Translation of these elements into appropriate depth maps
- Preservation of key narrative elements
- Optimisation of high relief rendering
The project is being developed in parallel with tactile image production experts who will test the effectiveness of the developed solutions.

The project offers the opportunity to:
- Working on a concrete problem of cultural accessibility
- Developing an innovative image interpretation system
- Collaborating with experts in the field of tactile representations
- Contributing to innovation in art accessibility