TLDR: This research paper explores the diverse and often ambiguous definitions of “hypothesis” across scientific fields, particularly within Natural Language Processing (NLP) and Natural Language Understanding (NLU) tasks. It categorizes modern interpretations (ideas, claims, proposals, formal expressions) and details related AI tasks like hypothesis extraction, verification, and generation. The paper advocates for clearer, standardized definitions to enable a machine-interpretable scholarly record and improve scientific knowledge assembly.
The term “hypothesis” is fundamental to scientific inquiry, yet its meaning has evolved significantly over centuries and varies widely across different scientific disciplines. In recent decades, with the rise of natural language processing (NLP) and natural language understanding (NLU), the interpretation of “hypothesis” has become even more diverse, leading to challenges in tasks that require machines to understand, extract, test, and generate these crucial statements.
A new research paper, “What Are Research Hypotheses?” by Jian Wu and Sarah Rajtmajer, delves into these varying definitions, particularly focusing on how the term is understood and operationalized within modern NLU tasks. The authors highlight the critical need for clear, well-structured definitions as we move towards a future where scientific knowledge can be easily interpreted by machines.
From Ancient Greece to Modern Science: The Evolution of Hypothesis
The concept of a hypothesis traces its roots back to ancient Greek philosophy, where it meant a “putting under” or a foundational assumption. Philosophers like Plato used it for temporary claims to explore implications, while Aristotle emphasized empirical verification, distinguishing hypotheses from established axioms. During the Scientific Revolution, figures like Galileo and Newton integrated hypotheses into the formal scientific method, stressing the importance of testing through observation and experiment. Later, philosophers such as Karl Popper and Thomas Kuhn further shaped our understanding, with Popper emphasizing falsifiability – the idea that a hypothesis can only be proven false, not true – and Kuhn viewing hypotheses within the context of scientific paradigms.
Hypotheses in the Age of AI: Diverse Interpretations
In contemporary NLP and NLU, the definition of a hypothesis has expanded to include several forms:
- Ideas as Hypotheses: Some research treats broad “ideas” or “future research ideas” as hypotheses, especially in hypothesis generation tasks. These are often preliminary notions intended to inspire further investigation.
- Claims as Hypotheses: A “claim” is typically an assertion of a finding. In some contexts, an untested claim is considered a hypothesis. This overlap is evident in tasks like scientific hypothesis evidencing and scientific claim verification, where the goal is to determine the relationship between a statement and supporting evidence.
- Hypothesis-Proposals: Advanced hypothesis generation models can produce not just a single statement, but a comprehensive “proposal-style” document. These proposals often include background, justification, and even test procedures, increasing transparency and guiding further research.
- Formal Expressions: Hypotheses can be formally structured, breaking them down into contexts, variables, and relationships. In NLU, they might be expressed as declarative statements, questions, or even as components within an “entailment tree” that shows how a hypothesis logically follows from a text corpus.
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Key Tasks Involving Hypotheses in NLP
The paper outlines several crucial tasks where understanding hypotheses is paramount:
- Natural Language Inference (NLI): This involves determining if a given text (premise) logically implies a hypothesis. Datasets like SciTail and EntailmentBank are used for scientific NLI.
- Hypothesis and Claim Extraction: The aim here is to automatically identify hypotheses or claims within scientific documents, whether from abstracts or full papers.
- Scientific Hypothesis Evidencing (SHE) and Scientific Claim Verification (SCV): These tasks focus on finding evidence in scientific literature that either supports or refutes a given hypothesis or claim. Datasets such as SciFact and DiscoveryBench facilitate this research.
- Scientific Hypothesis Generation: This exciting area uses large language models (LLMs) to automatically create new, testable scientific hypotheses or research ideas. The input for these models can range from keywords and research goals to raw data or background context, and the output can be a simple hypothesis, an enriched idea, or a full research proposal.
The authors emphasize that the variability in defining and operationalizing hypotheses, while historically acceptable, now poses a significant challenge for NLP and NLU tasks. They advocate for greater clarity and, where possible, standardization of hypothesis definitions to enable the creation of a “computable scholarly record.” This record would be a verifiable and extensible knowledge base, allowing for more robust scientific progress and efficient allocation of research resources. For more details, you can read the full paper here.
