The rise of AI-based technologies has led to increased interest in individualized text generation. Generative systems that can produce personalized responses that take into account factors such as the audience, creation context, and information needs are in high demand.
Understanding individualized text generation
Researchers have investigated the creation of customized text in a variety of settings, including reviews, chatbots, and social media. However, most existing work has focused on task-specific models that rely on domain-specific features or information. There is less attention on how to create a generic approach that can be used in any situation.
In the past, text generation was a relatively straightforward task. If you wanted to create a document, you would simply type it out from scratch. However, with the rise of artificial intelligence (AI), text generation is becoming increasingly sophisticated.
Individualized text generation
One of the most promising areas of AI research is individualized text generation. This is the task of generating text that is tailored to a specific individual or context. For example, an individualized email would be one that is specifically tailored to the recipient’s interests and preferences.
Challenges: There are a number of challenges associated with individualized text generation. One challenge is that it requires a large amount of data. In order to generate text that is tailored to a specific individual, the AI model needs to have a good understanding of that individual’s interests, preferences, and writing style.
Methods to improve individualized text generation
There are a number of methods that can be used to improve individualized text generation. One method is to train the AI model on a dataset of text that is specific to the individual or context. For example, if you want to generate personalized emails, you could train the AI model on a dataset of emails that have been sent and received by the individual.
Another method to improve individualized text generation is to use auxiliary tasks. Auxiliary tasks are additional tasks that are given to the AI model in addition to the main task of generating text. These tasks can help the AI model learn about the individual or context, which can then be used to improve the quality of the generated text.
LLMs for individualized text generation
Large Language Models (LLMs), although powerful, are typically trained on broad and general-purpose text data. This presents a unique set of hurdles to overcome. In this exploration, we delve into strategies to augment LLMs’ capacity for generating highly individualized text.
Training on specific data
One effective approach involves fine-tuning LLMs using data that is specific to the individual or context. Consider the scenario of crafting personalized emails. Here, the LLM can be fine-tuned using a dataset comprised of emails exchanged by the target individual. This tailored training equips the model with a deeper understanding of the individual’s language, tone, and preferences.
Harnessing auxiliary tasks
Another potent technique in our arsenal is the use of auxiliary tasks. These tasks complement the primary text generation objective and offer invaluable insights into the individual or context. By incorporating such auxiliary challenges, LLMs can significantly elevate the quality of their generated content.
Example: Author Identification: For instance, let’s take the case of an LLM tasked with generating personalized emails. An auxiliary task might involve identifying the author of an email from a given dataset. This seemingly minor task holds the key to a richer understanding of the individual’s unique writing style.
Google’s approach to individualized text generation
Recent research from Google proposes a generic approach to producing unique content by drawing on extensive linguistic resources. Their study is inspired by a common method of writing instruction that breaks down the writing process with external sources into smaller steps: research, source evaluation, summary, synthesis, and integration.
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Component
|
Description |
|---|---|
| Retrieval | The process of retrieving relevant information from a secondary repository of personal contexts, such as previous documents the user has written. |
| Ranking | The process of ranking the retrieved information for relevance and importance. |
| Summarization | The process of summarizing the ranked information into key elements. |
| Synthesis | The process of combining the key elements into a new document. |
| Generation | The process of generating the new document using an LLM. |
The Multi-Stage – Multi-Task Framework
To train LLMs for individualized text production, the Google team takes a similar approach, adopting a multistage multitask structure that includes retrieval, ranking, summarization, synthesis, and generation. Specifically, they use the title and first line of the current document to create a question and retrieve relevant information from a secondary repository of personal contexts, such as previous documents the user has written.
They then summarize the ranked results after ranking them for relevance and importance. In addition to retrieval and summarization, they synthesize the retrieved information into key elements, which are then fed into the LLM to generate the new document.
Improving the reading abilities of LLMs
It is a common observation in the field of language teaching that reading and writing skills develop hand in hand. Additionally, research shows that individual reading level and amount can be measured through author recognition activities, which correlate with reading proficiency.
These two findings led the Google researchers to create a multitasking environment where they added an auxiliary task asking the LLM to identify the authorship of a particular text to improve its reading abilities. They believe that by giving the model this challenge, it will be able to interpret the provided text more accurately and produce more compelling and tailored writing.
Evaluation of the proposed models
The Google team used three publicly available datasets consisting of email correspondence, social media debates, and product reviews to evaluate the performance of the proposed models. The multi-stage, multi-task framework showed significant improvements over several baselines across all three datasets.
Conclusion
The Google research team’s work presents a promising approach to individualized text generation with LLMs. The multi-stage, multi-task framework is able to effectively incorporate personal contexts and improve the reading abilities of LLMs, leading to more accurate and compelling text generation.
