Contextual Alignment

Contextual alignment in large language models (LLMs) focuses on improving model performance by aligning their responses with human intentions or desired outputs using contextual information, rather than solely relying on parameter adjustments. Current research explores various methods to achieve this, including techniques that leverage multiple contextual cues (e.g., examples, prompts, definitions) and architectures that facilitate inter- and intra-modal alignment, particularly in multimodal settings involving image and text. This research is significant because it offers more efficient and effective ways to improve LLM capabilities, particularly for low-resource languages and complex tasks like temporal referential dialogue, ultimately leading to more robust and human-aligned AI systems.