The past decade has been a witness to remarkable development in Artificial Intelligence as a promising field of research. One of the many tasks that AI is now capable of doing is reading and comprehension. AI reading is much more complicated when compared to other tasks like image recognition owing to the complexities of language. AI models have to consider aspects like figures of speech, synonyms, and the differences in context that can assign different meanings to the same word. This implies that AI models are required to access information that is not available to them in the source document itself. Moreover, they are required to analyze multiple phrases and summarize the synthesized information to correctly comprehend the meaning of the question.
Answering questions based on reading comprehension is one of the primary tasks included in any language-learning program. A similar technique is used to teach AI models to answer questions. In recent years several studies have pointed out that not only have AI models mastered this skill, they are also capable of out-performing average human standards set on public leaderboards.
Flaws and Its Solutions
However, there are certain flaws in the comprehension skills of these AI models. For instance, a recent study has indicated that AI models are not capable of generalizing adequately across data sets. Another finding from the same research suggests that AI models have a tendency of picking up on certain biases in the set of test materials and arrive at the right answer without necessarily performing AI reading comprehension. They have also been proved to be unable to handle variations in the wordings of the questions. To solve these problems, the following steps can be taken to improve AI reading and comprehension:
A. Look for generalizability
Make sure that the sample you have chosen cannot be generalized while testing your AI program for its reading comprehension. Report the performance of the AI and keep track of its responses across multiple data sets to ensure that the program is not just solving a single set.
B. Include variations
While designing your data set be sure to include a large number of variations in the question pattern. For this purpose, use words with similar meanings so that variations of the same question appear repeatedly on the data set.
C. Challenge the AI models
Remove questions that can be easily solved by your AI model and use challenging questions that would help in broadening their horizons of comprehension.
D. Test for shortcuts
Make sure that your models are not taking shortcuts. It is essential to keep in mind that good performance on your data sets does not ensure that your AI model is exhibiting reading comprehension.
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E. Use standardized formats while preparing your data sets
Try using standardized formats while preparing a new data set. This would enable you to perform easy examinations of cross data sets while checking the credibility of your model in reading AI files.
With AI progressing at a rapid rate, AI reading comprehension has become very effective. Ongoing research in this field indicates promising possibilities. Although the existence of some common flawed patterns of responses has been recorded, they can easily be rectified using the above-suggested steps. The goal should be to try and make the AI models resemble human cognition as closely as possible.