While using synthetic queries to augment corpus representation offers several advantages, there are notable limitations and drawbacks:
Quality of Synthetic Queries: The effectiveness of synthetic queries heavily relies on their quality. Poorly generated queries can introduce noise into the corpus representation, leading to decreased retrieval performance. To address this, implementing robust filtering mechanisms to assess the quality of generated queries before they are used in augmentation is crucial. Techniques such as semantic similarity checks or relevance scoring can help ensure that only high-quality queries are retained.
Overfitting to Synthetic Data: Relying too heavily on synthetic queries may lead to overfitting, where the model becomes too specialized in the synthetic data and performs poorly on real-world queries. To mitigate this risk, it is essential to maintain a balance between synthetic and real data during training. Regular evaluations on real-world datasets can help ensure that the model generalizes well.
Lack of Diversity: Synthetic queries may lack the diversity found in naturally occurring queries, potentially limiting the model's ability to handle varied user intents. To counter this, generating a wide range of synthetic queries that cover different aspects of the corpus and user intents can enhance diversity. Additionally, incorporating user feedback on query relevance can help refine the query generation process.
Domain Adaptation Challenges: Synthetic queries generated from one domain may not translate well to another, leading to domain adaptation issues. To address this, domain-specific instruction-tuning can be employed, where the model is fine-tuned on data relevant to the target domain. This approach ensures that the synthetic queries are more aligned with the specific characteristics of the domain.
Evaluation Metrics: The evaluation of synthetic queries can be challenging, as traditional metrics may not fully capture their effectiveness in improving retrieval performance. Developing new evaluation metrics that specifically assess the impact of synthetic queries on retrieval tasks can provide better insights into their utility and effectiveness.