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Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models
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Large language models (LLMs) have demonstrated remarkable capabilities in various scientific domains, from natural language processing to complex problem-solving tasks. Their ability to understand and generate human-like text has opened up new possibilities for advancing scientific research, enabling tasks such as data analysis, literature review, and even experimental design. One of the most promising applications of LLMs in this context is hypothesis generation, where they can identify novel research directions by analyzing existing knowledge. However, despite their potential, LLMs are prone to generating ``hallucinations'', outputs that are plausible-sounding but factually incorrect. Such a problem presents significant challenges in scientific fields that demand rigorous accuracy and verifiability, potentially leading to erroneous or misleading conclusions. To overcome these challenges, we propose KG-CoI (Knowledge Grounded Chain of Ideas), a novel system that enhances LLM hypothesis generation by integrating external, structured knowledge from knowledge graphs (KGs). KG-CoI guides LLMs through a structured reasoning process, organizing their output as a chain of ideas (CoI), and includes a KG-supported module for the detection of hallucinations. With experiments on our newly constructed hypothesis generation dataset, we demonstrate that KG-CoI not only improves the accuracy of LLM-generated hypotheses but also reduces the hallucination in their reasoning chains, highlighting its effectiveness in advancing real-world scientific research.
Forward citations
Cited by 7 Pith papers
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LLM judges of scientific ideas are measurably swayed by writing style; a style-detecting module reduces but does not remove the bias.
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DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations
Structuring LLM hypothesis generation around deductive-nomological explanation, causal processes, and universals is reported to beat direct prompting, with two generated ideas implemented as the CTAT and HALO algorithms.
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Interestingness First Classifiers
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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
A new benchmark (TruthHypo) and a knowledge-grounded hallucination detector (KnowHD) show that grounding scores can partially select truthful LLM-generated biomedical hypotheses, but the result is at risk from knowled...
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Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior
A logit-based method converts an LLM's numeric guesses into a calibrated prior over Pearson correlations and ranks expert-flagged hypotheses better than ranking by magnitude or by a fine-tuned RoBERTa classifier.
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AI Scientists Fail Without Strong Implementation Capability
AI scientist systems can propose ideas but cannot reliably implement and verify experiments, making the implementation gap, not idea generation, the current bottleneck.
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