What if in DPR (dense passage retrieval), the answer belongs to more than one passage?
Posted: Wed Aug 16, 2023 6:21 am
Dense Passage Retrieval (DPR) is a technique used in information retrieval to efficiently retrieve relevant passages of text from a large collection of documents for a given query. The goal of DPR is to identify the most relevant passages that contain the information needed to answer a question or fulfill a specific information need.
In the context of DPR, if the answer to a query belongs to more than one passage, the system can still be designed to handle such cases. Here's how it might work:
Multi-Passage Ranking: The retrieval process can be extended to retrieve multiple relevant passages rather than just a single one. The retrieved passages can be ranked based on their relevance scores to the query. This way, even if the answer spans multiple passages, the most relevant passages containing parts of the answer will receive higher ranks.
Answer Extraction and Aggregation: Once the relevant passages are retrieved, a subsequent step can involve extracting relevant information or answers from each passage. If the complete answer is distributed across multiple passages, the system can extract relevant segments from each passage and then aggregate them to form a comprehensive answer.
Answer Confidence and Verification: The system can assess the confidence level of each passage's answer segment and assign a score to it. This score can be based on factors such as context coherence, keyword matches, or linguistic patterns. Aggregating the segments with higher confidence scores can help create a more reliable and accurate answer.
Contextual Understanding: Some advanced models, like LLMs (Large Language Models), can understand the context of the query and passages more comprehensively. These models can potentially handle cases where the answer spans multiple passages and provide a coherent and contextually accurate answer.
Post-Processing and Analysis: In cases where the answer is distributed across multiple passages, post-processing techniques can be applied to analyze and combine the extracted segments effectively. This might involve resolving potential conflicts or contradictions between passages and ensuring that the final answer is coherent and accurate.
In all, while DPR is primarily focused on retrieving relevant passages for a query, additional steps and techniques can be employed to handle scenarios where the answer spans more than one passage. Advanced natural language processing methods and careful design of the retrieval and aggregation process can help ensure accurate and comprehensive answers in such cases.
In the context of DPR, if the answer to a query belongs to more than one passage, the system can still be designed to handle such cases. Here's how it might work:
Multi-Passage Ranking: The retrieval process can be extended to retrieve multiple relevant passages rather than just a single one. The retrieved passages can be ranked based on their relevance scores to the query. This way, even if the answer spans multiple passages, the most relevant passages containing parts of the answer will receive higher ranks.
Answer Extraction and Aggregation: Once the relevant passages are retrieved, a subsequent step can involve extracting relevant information or answers from each passage. If the complete answer is distributed across multiple passages, the system can extract relevant segments from each passage and then aggregate them to form a comprehensive answer.
Answer Confidence and Verification: The system can assess the confidence level of each passage's answer segment and assign a score to it. This score can be based on factors such as context coherence, keyword matches, or linguistic patterns. Aggregating the segments with higher confidence scores can help create a more reliable and accurate answer.
Contextual Understanding: Some advanced models, like LLMs (Large Language Models), can understand the context of the query and passages more comprehensively. These models can potentially handle cases where the answer spans multiple passages and provide a coherent and contextually accurate answer.
Post-Processing and Analysis: In cases where the answer is distributed across multiple passages, post-processing techniques can be applied to analyze and combine the extracted segments effectively. This might involve resolving potential conflicts or contradictions between passages and ensuring that the final answer is coherent and accurate.
In all, while DPR is primarily focused on retrieving relevant passages for a query, additional steps and techniques can be employed to handle scenarios where the answer spans more than one passage. Advanced natural language processing methods and careful design of the retrieval and aggregation process can help ensure accurate and comprehensive answers in such cases.