In recent years, advancements in deep learning have led to significant improvements in text generation capabilities, such as language models and transformer-based architectures. However, these models often struggle to produce coherent and relevant output when faced with long and complex input contexts. In this article, we propose a novel approach called Intuitive Highlighting (IH) that addresses this challenge by providing users with a dynamic highlighting scheme to guide the generation process.
Highlighting Scheme
The IH scheme is based on the concept of conditional context (c), which represents the difference between the predicted token and its corresponding attention mask. By appending mi = 0 and si = ¯si = f(xi) to the input sequence, we update the highlight mask m and contexts s, ¯s at each time step. This arrangement provides users with the flexibility to control the in-line requirements, such as adjusting the highlight guidance strength γ in Eq. (6).
Attention Activation
To further investigate token-wise correlations and their impact on generation results, we exclude sink tokens that dominate the attention score [40] and visualize cross-token self-attention score maps during inference. Users can manipulate the model’s output dynamically by adjusting the highlight guidance strength γ in Eq. (6). This capability can prove advantageous in a variety of tasks, such as generating descriptive captions for images or creating customized responses in conversational agents.
Reliable Description
When carrying out long text generation tasks, diverse formats and content of prompts are employed to steer the generation towards desired outcomes. However, prompt engineering often relies on empirical intuition and requires careful design of the context, making it less accessible for non-experts. As illustrated in the left part of Fig. 1, even meticulously crafted prompts can lead to unpredictable outputs that fail to meet the requirements.
Conclusion
In conclusion, Intuitive Highlighting offers a promising solution to improve the efficiency and effectiveness of text generation models. By providing users with a dynamic highlighting scheme, we enable them to control the generation process and steer it towards desired outcomes. Our approach has significant implications for a wide range of applications, from generating descriptive captions for images to creating customized responses in conversational agents. With Intuitive Highlighting, we demystify complex text generation and make it accessible to non-experts.
