Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:28:49.334512Z
Paper Citation Record · LEDGER
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning
As of 15 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.15717.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
52 of 52 outbound references displayed
- verified exact6
- verified fuzzy34
- unresolved10
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 41b878ad-7ebf-4384-aebd-e6bc462a0da6 · outbound
This paper cites Attention is All you Need [Internet].
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Attention is All you Need [Internet]
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:57.276819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b7a59abd-7264-4026-868e-40f46daa170e · outbound
This paper cites Towards Expert-Level Medical Question Answering with Large Language Models.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Towards Expert-Level Medical Question Answering with Large Language Models
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:45.191838Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6968ced4-5dd0-41b2-8e79-eae323c24c12 · outbound
This paper cites Towards Conversational Diagnostic AI.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Towards Conversational Diagnostic AI
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:45.332823Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2974194f-aad3-4e9a-aa91-77ced40dba2e · outbound
This paper cites Opportunities and challenges for ChatGPT and large language models in biomedicine and health.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Opportunities and challenges for ChatGPT and large language models in biomedicine and health
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:57.159541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ae2c89fd-8c14-49e8-8fe9-893bb4abe982 · outbound
This paper cites Benchmarking large language models for biomedical natural language processing applications and recommendations.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Benchmarking large language models for biomedical natural language processing applications and recommendations
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:57.005286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f16aec01-0270-4d23-b243-633889ed059a · outbound
This paper cites Radiology-Llama2: Best-in-Class Large Language Model for Radiology.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Radiology-Llama2: Best-in-Class Large Language Model for Radiology
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:45.636946Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b435dda8-be36-4702-80eb-6a71439ee956 · outbound
This paper cites Towards a general-purpose foundation model for computational pathology.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Towards a general-purpose foundation model for computational pathology
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:56.893597Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c4e2200e-a08d-424e-b627-ee91fbd05561 · outbound
This paper cites Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model [Internet].
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model [Internet]
Reference 8
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T15:28:51.092097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d0b17bca-a972-4431-becc-bd81ef17c053 · outbound
This paper cites Utilizing Large Language Models to Simplify Radiology Reports: a comparative analysis of ChatGPT3.5, ChatGPT4.0, Google Bard, and Microsoft Bing.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Utilizing Large Language Models to Simplify Radiology Reports: a comparative analysis of ChatGPT3.5, ChatGPT4.0, Google Bard, and Microsoft Bing
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:56.791303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation dd25c89c-50a6-45f5-9800-8396f2691f32 · outbound
This paper cites Can OpenAI’s New o1 Model Outperform Its Predecessors in Common Eye Care Queries? Ophthalmology Science [Internet] 2025 [cited 2025 Apr 26];5(4).
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Can OpenAI’s New o1 Model Outperform Its Predecessors in Common Eye Care Queries? Ophthalmology Science [Internet] 2025 [cited 2025 Apr 26];5(4)
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:56.626722Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4fbba0a1-01a9-43d9-acd1-4396a2de224a · outbound
This paper cites EyeGPT: Ophthalmic Assistant with Large Language Models.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning EyeGPT: Ophthalmic Assistant with Large Language Models
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:28:50.835793Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation dc9b8830-4855-42d4-8a87-377e0f522fe7 · outbound
This paper cites Can OpenAI o1’s Enhanced Reasoning Capabilities Extend to Ophthalmology? A Benchmark Study Across Large Language Models and Text Generation Metrics [Internet].
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Can OpenAI o1’s Enhanced Reasoning Capabilities Extend to Ophthalmology? A Benchmark Study Across Large Language Models and Text Generation Metrics [Internet]
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:56.497357Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation eb89e847-6645-4f25-a93c-09548d54c317 · outbound
This paper cites ChatGPT-Generated Differential Diagnosis Lists for Complex Case–Derived Clinical Vignettes: Diagnostic Accuracy Evaluation.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning ChatGPT-Generated Differential Diagnosis Lists for Complex Case–Derived Clinical Vignettes: Diagnostic Accuracy Evaluation
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:56.339531Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 77f3be25-38e3-4b8d-af47-cf236a59ef85 · outbound
This paper cites Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:56.164737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0612924a-dd29-47d7-a6e0-0b232010179e · outbound
This paper cites Can off-the-shelf visual large language models detect and diagnose ocular diseases from retinal photographs? BMJ Open Ophth [Internet] 2025 [cited 2025 May 15];10(1).
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Can off-the-shelf visual large language models detect and diagnose ocular diseases from retinal photographs? BMJ Open Ophth [Internet] 2025 [cited 2025 May 15];10(1)
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:56.057025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5a512d6d-13b5-485e-b7eb-0988035bb7fb · outbound
This paper cites ChatGPT: the future of discharge summaries? The Lancet Digital Health 2023;5(3):e107–8.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning ChatGPT: the future of discharge summaries? The Lancet Digital Health 2023;5(3):e107–8
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:55.890375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9523c4c7-4528-4ebe-ba60-3136efa9f739 · outbound
This paper cites Large Language Models Seem Miraculous, but Science Abhors Miracles.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Large Language Models Seem Miraculous, but Science Abhors Miracles
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:55.584987Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ae5f0ee8-f75f-4af2-9fa2-603115a10bf7 · outbound
This paper cites Medical Ethics of Large Language Models in Medicine.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Medical Ethics of Large Language Models in Medicine
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:55.484975Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 64f78800-3061-4837-8c3b-de7ea129d70a · outbound
This paper cites Google for Developers.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Google for Developers
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:55.354156Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 82a1fe2c-2b66-4048-a245-fd54a38c2915 · outbound
This paper cites Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:55.225606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ab00acb6-03f0-49cb-918d-09ea297d8ad5 · outbound
This paper cites Measuring Massive Multitask Language Understanding.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Measuring Massive Multitask Language Understanding
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:46.831673Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8afb170-bfd4-4613-84da-88c6a6f8b200 · outbound
This paper cites HealthBench: Evaluating Large Language Models Towards Improved Human Health.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning HealthBench: Evaluating Large Language Models Towards Improved Human Health
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:55.030073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ceb9921a-cccb-40af-90b1-b0da72ed42a0 · outbound
This paper cites PathVQA: 30000+ Questions for Medical Visual Question Answering.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning PathVQA: 30000+ Questions for Medical Visual Question Answering
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:54.911878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6c297985-bf6d-4bb5-9c5a-ad39439d5b36 · outbound
This paper cites MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:54.766328Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d8c35cad-0cd8-4378-9ce3-dd0d82383e72 · outbound
This paper cites What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:47.108780Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 620c27bb-70d6-4918-8fb5-5eef302943b4 · outbound
This paper cites EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:47.162607Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 793f4a7b-ebd2-484d-b992-0539ba088dbf · outbound
This paper cites Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:28:50.529350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a8106e3a-06e1-46d6-bf98-58024df53215 · outbound
This paper cites Unveiling the clinical incapabilities: a benchmarking study of GPT-4V(ision) for ophthalmic multimodal image analysis.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Unveiling the clinical incapabilities: a benchmarking study of GPT-4V(ision) for ophthalmic multimodal image analysis
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:54.660686Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7dab4c86-1fba-44e8-97ed-0aa0aeaf3785 · outbound
This paper cites Evaluating the Performance of ChatGPT in Ophthalmology: An Analysis of Its Successes and Shortcomings.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Evaluating the Performance of ChatGPT in Ophthalmology: An Analysis of Its Successes and Shortcomings
Reference 29
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-06T15:28:50.372241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7cc5c6d7-369d-43b7-81c6-7381732dce9c · outbound
This paper cites Evaluating Large Language Models in Ophthalmology.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Evaluating Large Language Models in Ophthalmology
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:28:50.050972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 71bd979c-52ef-432c-96cc-8e3fa3c7fbc4 · outbound
This paper cites OphthBench: A Comprehensive Benchmark for Evaluating Large Language Models in Chinese Ophthalmology.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning OphthBench: A Comprehensive Benchmark for Evaluating Large Language Models in Chinese Ophthalmology
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:28:49.862916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7a40c24a-e822-4dee-85cc-54da13ee7c41 · outbound
This paper cites EYE-Llama, an in-domain large language model for ophthalmology.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning EYE-Llama, an in-domain large language model for ophthalmology
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:54.534407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ac32d7b4-1998-4ac3-9e4d-29eb344278e7 · outbound
This paper cites BioASQ-QA: A manually curated corpus for Biomedical Question Answering.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning BioASQ-QA: A manually curated corpus for Biomedical Question Answering
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:54.420785Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a14f5cc9-a325-4875-a478-471344818985 · outbound
This paper cites Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:54.253238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2feb1c9a-705f-40ea-94fd-ce61c8dc9cdb · outbound
This paper cites PubMedQA: A Dataset for Biomedical Research Question Answering.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning PubMedQA: A Dataset for Biomedical Research Question Answering
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:54.129247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d80f5379-265e-4c45-8b36-9a23073f0a87 · outbound
This paper cites Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:53.878466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 89a81b06-6227-4b91-8831-4b97f4ad7987 · outbound
This paper cites How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:53.578158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d4321c0a-7ddc-4671-b69d-38d1c30a9009 · outbound
This paper cites Benchmarking Next-Generation Reasoning-Focused Large Language Models in Ophthalmology: A Head-to-Head Evaluation on 5,888 Items.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Benchmarking Next-Generation Reasoning-Focused Large Language Models in Ophthalmology: A Head-to-Head Evaluation on 5,888 Items
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:28:49.676971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 56f9c3ce-b564-4ea3-9fa2-20ff9cf01447 · outbound
This paper cites ROUGE: A Package for Automatic Evaluation of Summaries [Internet].
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning ROUGE: A Package for Automatic Evaluation of Summaries [Internet]
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:53.374598Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2f114fce-6175-467b-9f64-0ea339905859 · outbound
This paper cites BERTScore: Evaluating Text Generation with BERT.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning BERTScore: Evaluating Text Generation with BERT
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:48.277853Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 803eb354-c9b5-4c9a-aecf-cf64380105c3 · outbound
This paper cites BARTScore: Evaluating Generated Text as Text Generation.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning BARTScore: Evaluating Generated Text as Text Generation
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:48.347345Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 005638be-e921-4c09-bc34-fda54f712046 · outbound
This paper cites AlignScore: Evaluating Factual Consistency with a Unified Alignment Function.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning AlignScore: Evaluating Factual Consistency with a Unified Alignment Function
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:28:48.415628Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 901cef58-08f4-4bde-86c0-d97e2fd9bd10 · outbound
This paper cites A visual-language foundation model for computational pathology.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning A visual-language foundation model for computational pathology
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:53.077371Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a3d77380-73a4-477c-a265-12412b9e81ff · outbound
This paper cites Developing and Evaluating Large Language Model– Generated Emergency Medicine Handoff Notes.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Developing and Evaluating Large Language Model– Generated Emergency Medicine Handoff Notes
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:52.795191Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a8c674eb-fc2d-4269-8c39-fa32849d718d · outbound
This paper cites CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:52.523882Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c9a1c9a2-34c0-41da-a883-64f5401f8426 · outbound
This paper cites Reducing hallucinations of large language models via hierarchical semantic piece.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Reducing hallucinations of large language models via hierarchical semantic piece
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:52.356278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6c1cbc6e-4805-4862-b218-907ab14c75b3 · outbound
This paper cites Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:52.100594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8ddb41ec-a6c3-471f-b196-f0edbf68b762 · outbound
This paper cites LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
Reference 48
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T15:28:48.930283Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b030e2a6-9b2d-4496-a218-a08b76dbfc17 · outbound
This paper cites medical QA dataset.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning medical QA dataset
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:51.799503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a5402410-20ca-46c3-9d2d-a7920e64ef30 · outbound
This paper cites Each question offers four options, out of which only one is correct.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Each question offers four options, out of which only one is correct
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:51.668008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation af61e65b-595e-4430-b810-8a7cd2432b2e · outbound
This paper cites glaucoma,.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning glaucoma,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T15:28:51.495773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c2becfc3-52ba-434d-9744-9d5071f96789 · outbound
This paper cites an unresolved cited work.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning Unresolved cited work
Reference 52
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T15:28:51.338945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.