Strategies for mitigating token limits issue involve techniques to enable large language models (LLMs) to focus on relevant parts of text when processing lengthy documents.
Hierarchical Attention Mechanisms
This technique allows the LLM to focus attention on specific parts of the retrieved text considered most relevant to the generation task, even if the entire document can't be processed at once.
Hierarchical attention mechanism is a sophisticated method used in natural language processing (NLP) tasks, especially when dealing with large documents that exceed the token limits of a language model. This technique breaks down the document into smaller segments and uses attention mechanisms to focus on the most relevant parts at various levels of detail.
Let's say we have a large document such as a research paper. The document contains a lot of information, but the language model can only process a limited number of tokens at a time due to computational constraints.
With hierarchical attention mechanisms, the document is divided into sections, paragraphs, or even sentences. At each level of hierarchy, the attention mechanism helps the model determine which parts are most relevant to the query.
For example, at the highest level of hierarchy, the attention mechanism might focus on identifying the most relevant sections of the document. Once these sections are identified, the attention mechanism can further zoom in to focus on specific paragraphs or sentences, specific phrases or keywords within those sections that contain crucial information for generating a response.
By using this mechanism, the language model can effectively prioritize its attention on the most informative segments of the document. This approach enhances the model's ability to generate accurate and contextually relevant responses while efficiently managing computational resources.
Document Summarization
Pre-processing retrieved documents with summarization techniques can help condense important information within the token limit.
Multi-Pass Generation
The generation process can be split into stages, where the LLM considers different portions of the retrieved text in each pass. By doing this iteratively, the model gradually builds a more thorough understanding of the input and context, which ultimately enhances the quality of the generated output.