Paper Citation Record · LEDGER

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

As of 8 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 2 inbound Pith citation observations for arXiv:2505.14599.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.14599 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:35:44.323361Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:33:46.563630Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T17:28:44.597412Z

Reference resolution

61 of 61 outbound references displayed

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External citation measurements

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Outbound references

Observation 5197324c-4360-41e6-9a7a-06d6e70faee5 · outbound

This paper cites Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment

Reference 1

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Observation 98fee1f0-be9b-47d0-b0d5-203e00306dc6 · outbound

This paper cites GPT-4 Technical Report.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models GPT-4 Technical Report

Reference 2

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This paper cites ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models

Reference 3

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This paper cites Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

Reference 4

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This paper cites MARG: Multi-Agent Review Generation for Scientific Papers.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models MARG: Multi-Agent Review Generation for Scientific Papers

Reference 5

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Observation d6b4a4ee-51b0-425e-bbdc-a078bd72cb90 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Towards A Rigorous Science of Interpretable Machine Learning

Reference 6

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This paper cites The Llama 3 Herd of Models.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models The Llama 3 Herd of Models

Reference 7

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This paper cites On the creativity of large language models.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models On the creativity of large language models

Reference 8

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This paper cites Forecasting high-impact research topics via machine learning on evolving knowledge graphs.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Forecasting high-impact research topics via machine learning on evolving knowledge graphs

Reference 9

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This paper cites Embracing foundation models for advancing scientific discovery.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Embracing foundation models for advancing scientific discovery

Reference 10

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Observation ce3f7d09-122c-4d91-8f0b-3a17a80fa916 · outbound

This paper cites Williams, Stefan Bekiranov, and Aidong Zhang.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Williams, Stefan Bekiranov, and Aidong Zhang

Reference 11

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This paper cites Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas

Reference 12

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This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 13

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This paper cites Autonomous llm-driven research—from data to human-verifiable research papers.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Autonomous llm-driven research—from data to human-verifiable research papers

Reference 14

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This paper cites A survey on knowledge graphs: Representation, acquisition, and applications.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models A survey on knowledge graphs: Representation, acquisition, and applications

Reference 15

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This paper cites Entry-level guide to the use of large language models for medical research.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Entry-level guide to the use of large language models for medical research

Reference 16

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This paper cites Large language models versus natural language understanding and generation.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Large language models versus natural language understanding and generation

Reference 17

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This paper cites Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network

Reference 18

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This paper cites MyCrunchGPT: A chatGPT assisted framework for scientific machine learning.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models MyCrunchGPT: A chatGPT assisted framework for scientific machine learning

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models PaperQA: Retrieval-Augmented Generative Agent for Scientific Research

Reference 20

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 21

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents

Reference 22

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Learning entity and relation embeddings for knowledge graph completion

Reference 23

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models A Survey on Graph Classification and Link Prediction based on GNN

Reference 24

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Conversational drug editing using retrieval and domain feedback

Reference 25

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This paper cites Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011--2022).

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011--2022)

Reference 26

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Improving biomedical information retrieval with neural retrievers

Reference 27

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This paper cites Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller

Reference 28

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Think-on-graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval

Reference 29

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This paper cites Explainable ai is dead, long live explainable ai! hypothesis-driven decision support using evaluative ai.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Explainable ai is dead, long live explainable ai! hypothesis-driven decision support using evaluative ai

Reference 30

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This paper cites Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models A review of relational machine learning for knowledge graphs

Reference 32

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Can chatgpt be used to generate scientific hypotheses? Journal of Materiomics , 10(3):578--584, 2024

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Graph Retrieval-Augmented Generation: A Survey

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Large Language Models are Zero Shot Hypothesis Proposers

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Large language models as biomedical hypothesis generators: A comprehensive evaluation

Reference 36

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Human-LLM Compound System for Scientific Ideation through Facet Recombination and Novelty Evaluation

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models A review on large language models: Architectures, applications, taxonomies, open issues and challenges

Reference 38

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models The probabilistic relevance framework: Bm25 and beyond

Reference 39

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Knowledge Graph Large Language Model (KG-LLM) for Link Prediction

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

Reference 41

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Colidr: Concept learning using aggregated disentangled representations

Reference 42

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models A self-explaining neural architecture for generalizable concept learning

Reference 43

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Language agents achieve superhuman synthesis of scientific knowledge

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models SciMON: Scientific Inspiration Machines Optimized for Novelty

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation

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Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Chain-of-thought prompting elicits reasoning in large language models

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This paper cites Pubtator 3.0: an ai-powered literature resource for unlocking biomedical knowledge.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Pubtator 3.0: an ai-powered literature resource for unlocking biomedical knowledge

Reference 49

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This paper cites Generating Scientific Claims for Zero-Shot Scientific Fact Checking.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Generating Scientific Claims for Zero-Shot Scientific Fact Checking

Reference 50

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This paper cites Dynamic link prediction using graph representation learning with enhanced structure and temporal information.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Dynamic link prediction using graph representation learning with enhanced structure and temporal information

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:35:44.868411Z

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This paper cites Benchmarking retrieval-augmented generation for medicine.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Benchmarking retrieval-augmented generation for medicine

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:35:44.854624Z

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This paper cites Improving retrieval-augmented generation in medicine with iterative follow-up questions.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Improving retrieval-augmented generation in medicine with iterative follow-up questions

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:35:44.841311Z

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This paper cites Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models

Reference 54

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This paper cites Large Language Models for Automated Open-domain Scientific Hypotheses Discovery.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Large Language Models for Automated Open-domain Scientific Hypotheses Discovery

Reference 55

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This paper cites Large language models for rediscovering unseen chemistry scientific hypotheses.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Large language models for rediscovering unseen chemistry scientific hypotheses

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:35:44.828609Z

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This paper cites Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:35:44.379404Z

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Observation 55e1809b-e6eb-4843-b4e9-b048cf3c4d79 · outbound

This paper cites Link prediction based on graph neural networks.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Link prediction based on graph neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:35:44.816240Z

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Observation 342c9668-7aaf-4f82-8ce1-61aa02e612b4 · outbound

This paper cites Goal driven discovery of distributional differences via language descriptions.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Goal driven discovery of distributional differences via language descriptions

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:35:44.803688Z

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Observation dcecd143-52f7-4e3a-8f13-682e8c310db2 · outbound

This paper cites Hypothesis Generation with Large Language Models.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Hypothesis Generation with Large Language Models

Reference 60

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This paper cites write newline.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models write newline

Reference 61

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Pith citing papers

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Interestingness First Classifiers cites this paper.

Interestingness First Classifiers Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

Reference 63

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BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation cites this paper.

BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.598968Z

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