Streamlining NASA Research Retrieval by Enhancing RAG-Inspired Search with Hypothetical Abstract Generation
This paper builds directly on our prior work presented at ICES 2025, which introduced a hybrid retrieval system combining sparse lexical retrieval (BM25), dense vector embeddings, and Reciprocal Rank Fusion to streamline NASA research search and retrieval. In that work, experimental results revealed a critical limitation: short-form queries, typical of user search behavior, consistently underperformed longer, semantically-rich queries across all retrieval strategies, with sparse retrieval exhibiting the largest degradation. This performance gap was attributed to a fundamental mismatch between the brevity of user queries and the length, jargon density, and conceptual structure of technical abstracts within the corpus. To address this limitation, this work proposes and evaluates the integration of a Hypothetical Document Embeddings (HyDE) layer into the retrieval pipeline. Rather than embedding the raw user query, the system first generates a hypothetical abstract that reflects the theoretical content, terminology, and structure of a relevant technical paper's abstract, corresponding to the intent of the user’s query. This generated abstract is then used as the retrieval query for both sparse and dense search methods. By increasing semantic density and domain-specific language, the hypothetical document theoretically improves alignment with indexed abstracts in both term-frequency and embedding space. We integrate this HyDE-based approach into the existing modular hybrid retrieval architecture and evaluate its impact on retrieval effectiveness across varying query lengths and retrieval strategies. Contrary to expectations, the results demonstrate that hypothetical document generation does not improve ranking performance for short-form or long-form queries.