Beyond Single Answers: New Benchmark Challenges AI to Understand Complex Scientific Queries

Author: Denis Avetisyan

A new study introduces a benchmark and retrieval framework designed to improve how AI systems answer scientific questions with multiple underlying intents.

Researchers present the MuISQA benchmark for multi-intent scientific question answering and an intent-aware retrieval framework to enhance performance through diverse query generation and effective evidence fusion.

Complex scientific questions frequently demand integration of information across multiple, often implicit, intents-a challenge for current retrieval-augmented generation (RAG) systems typically designed for single-intent queries. To address this limitation, we introduce ‘MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering’ and a novel benchmark designed to evaluate RAG’s ability to synthesize heterogeneous evidence. Our intent-aware framework leverages large language models to decompose questions, retrieve supporting passages for each underlying intent, and then effectively fuse this information, consistently outperforming conventional approaches in both retrieval accuracy and evidence coverage. Could this approach unlock more comprehensive and nuanced answers to complex scientific inquiries, ultimately accelerating discovery?

The Inherent Limitations of Single-Intent Question Answering

Conventional question answering systems frequently falter when confronted with inquiries demanding the consolidation of information from diverse sources, a critical limitation within complex scientific fields. These systems are typically designed to address single, explicit questions, struggling to identify and integrate multiple underlying informational needs within a single query. For example, a question about the efficacy of a drug might implicitly require information regarding its mechanism of action, potential side effects, and relevant patient demographics – details not always directly stated but crucial for a comprehensive answer. This inability to synthesize multi-faceted requests hinders their application in scientific research, where nuanced understanding and cross-referencing of data are paramount, and often necessitates manual curation of answers from a variety of sources.

Current scientific question answering datasets frequently present challenges for truly evaluating a system’s reasoning capabilities because they often prioritize single-intent queries. This simplification overlooks the reality that scientific inquiry rarely centers on isolated facts; instead, it commonly demands the integration of information from diverse sources to address multifaceted questions. A robust evaluation necessitates datasets containing questions requiring systems to not only identify relevant passages but also to synthesize them, reconcile potentially conflicting information, and infer connections between seemingly disparate concepts – a level of nuance largely absent in existing benchmarks. Consequently, high performance on current datasets doesn’t necessarily translate to genuine understanding or the capacity to tackle the complex, multi-faceted informational needs inherent in scientific research, hindering progress towards truly intelligent question answering systems.

MuISQA: A Benchmark Designed for Nuance

The MuISQA benchmark is specifically engineered to assess Retrieval-Augmented Generation (RAG) systems in the domain of multi-intent scientific question answering. Current question answering systems often struggle with inquiries requiring the integration of multiple, related concepts to formulate a complete response. MuISQA addresses this limitation by presenting questions designed to necessitate the identification and synthesis of information from diverse sources to satisfy multiple underlying intents within a single query. This focus pushes the boundaries of existing QA capabilities by demanding more sophisticated retrieval and reasoning mechanisms than those evaluated by standard benchmarks, which typically concentrate on single-intent questions. The benchmark’s design aims to highlight deficiencies in RAG systems’ abilities to discern nuanced meaning and integrate information effectively, fostering development of more robust and accurate scientific QA solutions.

The MuISQA dataset utilizes pre-annotation performed by large language models, specifically DeepSeek-V3, to establish a high-quality foundation for benchmark evaluations. This approach involves LLM-assisted labeling of questions and relevant supporting documents, reducing the reliance on extensive manual annotation. The resulting dataset contains over 1,200 question-answer pairs sourced from scientific publications, with each question designed to require information synthesis from multiple supporting passages. This LLM-driven pre-annotation process allows for the creation of a robust and challenging benchmark capable of rigorously evaluating the performance of retrieval-augmented generation (RAG) systems on complex scientific reasoning tasks, while also enabling scalability and reducing annotation costs.

Current question answering benchmarks often address single, isolated intents within a query. MuISQA differentiates itself by explicitly modeling the multi-intent nature of many scientific questions; a single question may require identifying a definition, comparing experimental results, and extracting a specific value, all simultaneously. This is achieved through annotation guidelines that allow for the labeling of multiple, distinct intents within a single question-answer pair. The dataset reflects this complexity, with each question potentially having multiple relevant answers, each satisfying a different identified intent. This approach provides a more realistic evaluation of retrieval-augmented generation (RAG) systems, as it forces them to disentangle complex requests and synthesize information from multiple sources to fulfill all underlying intentions.

Deconstructing Intent: A Hypothetical Query Generation Strategy

Hypothetical Query Generation (HQG) utilizes Large Language Models (LLMs) to proactively formulate potential answers to a user’s initial query before accessing a knowledge source. This process doesn’t directly retrieve information; instead, the LLM generates a full-form response as if the information were already known. Subsequently, this generated answer is algorithmically decomposed into a series of distinct, intent-specific queries. Each of these decomposed queries represents a focused information need embedded within the broader hypothetical answer, allowing the Retrieval-Augmented Generation (RAG) system to target precise data points rather than relying solely on keyword matches to the original query. This decomposition ensures comprehensive information retrieval by uncovering implicit relationships and contextual details within the anticipated response.

Hypothetical Query Generation extends the scope of information retrieval beyond direct keyword matches by proactively identifying and formulating queries that address implicitly related concepts. This process doesn’t limit search to terms explicitly present in the user’s initial request; instead, the system generates queries representing potential underlying needs or associated information gaps. By considering these derived queries, the RAG system accesses a broader range of documents, increasing the likelihood of identifying relevant context and providing a more comprehensive response, even if that information isn’t directly stated in the original query.

Traditional Retrieval-Augmented Generation (RAG) systems often rely on keyword matching to identify relevant documents, which can limit performance when user intent is nuanced or expressed implicitly. Generating intent-specific queries addresses this limitation by decomposing the original query into multiple, focused sub-queries that represent distinct facets of the user’s underlying information need. This approach allows the RAG system to retrieve documents that may not contain the original keywords but are nevertheless relevant to a specific aspect of the user’s intent. By broadening the retrieval scope beyond lexical overlap, the system captures a more comprehensive range of potentially useful information, improving the accuracy and completeness of generated responses.

Refining Retrieval: Advanced RAG Techniques in Action

Reciprocal Rank Fusion (RRF) is employed as a post-retrieval ranking algorithm to consolidate results from multiple query formulations. The method assigns a score to each document based on the reciprocal rank of its appearance across all queries; specifically, the score is calculated as the sum of $1/rank_{i}$ for each query $i$ where the document appears. This aggregated score effectively prioritizes documents consistently ranked highly across different, yet semantically related, queries. By combining evidence from multiple perspectives, RRF improves both precision, by elevating highly relevant documents, and recall, by ensuring that potentially relevant documents are not overlooked due to variations in query phrasing.

To enhance retrieval performance, the system incorporates both HyDE (Hypothetical Document Embeddings) and query rewriting techniques. HyDE generates embeddings based on hypothetical answers to the query, allowing for a more semantically relevant document retrieval process. Query rewriting involves reformulating the original query into multiple variations that capture different facets of the user’s intent; these rewritten queries are then used to broaden the search and increase the likelihood of retrieving pertinent documents. The combined effect of these methods is to improve the system’s ability to identify and access the most relevant knowledge sources for a given query, going beyond simple keyword matching.

Evaluations across multiple question answering datasets demonstrate significant performance improvements resulting from the implemented retrieval techniques. Specifically, the HotpotQA dataset yielded gains of +7.7% in Exact Match (EM) and +9.5% in F1 score. Performance on the Natural Questions (NQ) dataset improved by +4.6% F1, while the TriviaQA dataset saw a +3.7% increase in F1 score. These results indicate consistent and measurable enhancements in retrieval accuracy and relevance across diverse knowledge domains and question types.

Measuring True Retrieval Quality: Beyond Simple Precision

Information Recall Rate (IRR) serves as a pivotal metric in evaluating the efficacy of Retrieval-Augmented Generation (RAG) systems, moving beyond simple precision to comprehensively assess retrieval coverage and completeness. Unlike traditional measures that prioritize the relevance of retrieved documents, IRR focuses on capturing the extent to which a system successfully identifies all relevant information pertaining to a query. This is achieved by comparing the system’s retrieved set against a ground truth of relevant passages, calculating the proportion of essential information successfully recovered. A higher IRR indicates a more robust retrieval process, ensuring that the system doesn’t overlook crucial details, and ultimately leading to more informed and accurate responses. The metric is particularly valuable in complex scientific domains where comprehensive information gathering is paramount, and even minor omissions can significantly impact the quality of generated answers.

Vector entropy serves as a crucial diagnostic tool in evaluating the nuanced behavior of retrieval systems. This metric quantifies the informational complexity and diversity embedded within the vector representations of queries – essentially, how much ‘information’ is captured and how broadly that information is distributed. A higher vector entropy suggests the query representation encompasses a wider range of relevant concepts, potentially indicating a more robust and comprehensive search strategy. Conversely, low entropy might signal that the query is overly focused or that the system struggles to capture the full semantic scope of the user’s intent. By analyzing vector entropy alongside retrieval performance, researchers gain valuable insights into whether a system’s success stems from genuinely understanding the query, or simply matching keywords, ultimately leading to more refined and intelligent information retrieval models.

Evaluations of the retrieval-augmented generation (RAG) systems, performed using datasets like MuISQA and a range of established benchmarks, reveal a significant improvement in retrieval coverage compared to existing methodologies. This enhanced performance is particularly notable when addressing the complexities inherent in multi-intent scientific question answering, where queries often require integrating information from diverse sources to formulate a complete response. The systems consistently demonstrate an ability to identify and incorporate a greater breadth of relevant information, leading to more comprehensive and accurate answers. This suggests a substantial advancement in handling the nuanced information needs characteristic of scientific inquiry, and confirms the effectiveness of the approach in navigating the challenges posed by complex, multi-faceted questions.

The pursuit of robust scientific question answering, as exemplified by MuISQA, demands a precision mirroring mathematical proof. The framework’s intent-aware retrieval, generating diverse queries and fusing evidence, resonates with a fundamental tenet of elegant solutions. Andrey Kolmogorov once stated, “The shortest and most obvious solution is usually the best.” This echoes the paper’s objective – to distill complex scientific inquiries into readily accessible knowledge through a streamlined RAG system. The multi-intent aspect further emphasizes the need for logical decomposition, ensuring each facet of the question receives a rigorous and verifiable answer, ultimately striving for a ‘correct’ solution rather than a merely functional one.

What’s Next?

The introduction of the MuISQA benchmark, while a necessary step, merely highlights the fragility of current retrieval-augmented generation (RAG) systems when confronted with nuance. The notion that a system can reliably dissect multi-intent queries and synthesize coherent answers remains, at best, optimistic. The observed improvements through intent-aware retrieval are not, in themselves, proof of understanding, but rather skillful manipulation of statistical correlations. Reproducibility, of course, is paramount; any reported gains must be demonstrably consistent across varied implementations and hardware configurations.

A critical limitation lies in the assumption that relevant evidence can be adequately captured by textual retrieval. Scientific knowledge is often embedded in figures, equations, and experimental designs – modalities largely ignored by this work. Future investigations should address the integration of these non-textual elements, moving beyond simple keyword matching to true semantic understanding. The pursuit of ‘diversity’ in query generation is also suspect. Diversity without a grounding in logical validity is merely noise, potentially obscuring the correct answer rather than illuminating it.

Ultimately, the field requires a shift in focus. Performance metrics based solely on superficial similarity to ground truth answers are insufficient. A more rigorous approach would demand formal verification of the generated reasoning, ensuring that conclusions are logically derived from the retrieved evidence. Until then, these systems remain sophisticated pattern matchers, not genuine scientific reasoners.

Original article: https://arxiv.org/pdf/2511.16283.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/