Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:29:35.638652Z
Paper Citation Record · LEDGER
As of 14 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 2 inbound Pith citation observations for arXiv:2411.08715.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:29:35.638652Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:29:35.603284Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-09T13:41:13.040098Z
85 of 85 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e09c6dcc-4d40-406a-a6ee-835636c270be · outbound
Retrieval Augmented Recipe Generation GPT-4 Technical Report
Reference 1
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Observation 051839e9-fb98-4f66-847f-fefed63c8515 · outbound
Retrieval Augmented Recipe Generation Flamingo: a visual language model for few-shot learning
Reference 2
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Observation b0452c6a-8471-41ab-8c24-624c93f9f3ad · outbound
Retrieval Augmented Recipe Generation Food-101–mining discriminative components with random forests
Reference 3
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Retrieval Augmented Recipe Generation Visualgpt: Data-efficient adaptation of pretrained language models for image captioning
Reference 4
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Retrieval Augmented Recipe Generation Bench- marking large language models in retrieval-augmented gen- eration
Reference 5
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Observation 4c1d5aae-f663-4a38-8b8c-4e92370b1826 · outbound
Retrieval Augmented Recipe Generation Deep-based ingredi- ent recognition for cooking recipe retrieval
Reference 6
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Retrieval Augmented Recipe Generation A study of multi-task and region-wise deep learning for food ingredient recognition
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Retrieval Augmented Recipe Generation MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
Reference 8
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Retrieval Augmented Recipe Generation Cross- modal recipe retrieval with rich food attributes
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Retrieval Augmented Recipe Generation Image search with text feedback by visiolinguistic attention learn- ing
Reference 10
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Retrieval Augmented Recipe Generation Fire: Food image to recipe generation
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Retrieval Augmented Recipe Generation Food recognition: a new dataset, experiments, and re- sults
Reference 12
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Retrieval Augmented Recipe Generation Food logging: an information literacy perspective
Reference 13
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Retrieval Augmented Recipe Generation Instructblip: Towards general- purpose vision-language models with instruction tuning
Reference 14
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Retrieval Augmented Recipe Generation Bert: Pre-training of deep bidirectional trans- formers for language understanding
Reference 15
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Retrieval Augmented Recipe Generation An image is worth 16x16 words: Trans- formers for image recognition at scale
Reference 16
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Observation 16ff01c4-df22-49e0-aceb-dd411acb38e8 · outbound
Retrieval Augmented Recipe Generation Exploiting food choice biases for healthier recipe recommen- dation
Reference 17
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Retrieval Augmented Recipe Generation Chef transformer,
Reference 18
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Observation 27fac8fa-6873-4e67-b003-1035b2e92eaa · outbound
Retrieval Augmented Recipe Generation Intelligent food planning: personalized recipe recommendation
Reference 19
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Retrieval Augmented Recipe Generation Go figure: A meta evaluation of factuality in summarization
Reference 20
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Observation 4e7e4db4-08c8-4365-8497-fb5f13d5370e · outbound
Retrieval Augmented Recipe Generation Retrieval-Augmented Generation for Large Language Models: A Survey
Reference 21
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Observation 96001a8b-7b91-46d6-adaa-3a3370fbafac · outbound
Retrieval Augmented Recipe Generation Navigating weight prediction with diet diary
Reference 22
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Retrieval Augmented Recipe Generation Cross-modal retrieval aug- mentation for multi-modal classification
Reference 23
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Retrieval Augmented Recipe Generation Retrieval augmented language model pre- training
Reference 24
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Retrieval Augmented Recipe Generation Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, and Lav R Varshney
Reference 25
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Retrieval Augmented Recipe Generation Lora: Low-rank adaptation of large language models
Reference 26
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Observation 22c6902b-6270-4f2c-8fbe-e8ecffc672d7 · outbound
Retrieval Augmented Recipe Generation Reveal: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge mem- ory
Reference 27
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Observation 28f46468-5497-40dc-a03e-07601d5e6871 · outbound
Retrieval Augmented Recipe Generation Multi-scale multi-view deep feature aggregation for food recognition
Reference 28
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Observation e099eac3-b50d-4bd9-9806-6b11b298f009 · outbound
Retrieval Augmented Recipe Generation Active retrieval augmented generation
Reference 29
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Observation 7c50faac-f6e8-4770-8092-775e6f1390c4 · outbound
Retrieval Augmented Recipe Generation RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models
Reference 30
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Observation dde8b645-7b8e-4830-a2bb-d430fbc4bb02 · outbound
Retrieval Augmented Recipe Generation Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
Reference 31
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Retrieval Augmented Recipe Generation Two-stage visual cues en- hancement network for referring image segmentation
Reference 32
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Observation 16432e8c-11ae-4110-8cbf-15c8e11bc4ab · outbound
Retrieval Augmented Recipe Generation LISA: Reasoning Segmentation via Large Language Model
Reference 33
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Retrieval Augmented Recipe Generation Pre-training via paraphrasing
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Retrieval Augmented Recipe Generation Llava-med: Training a large language- and-vision assistant for biomedicine in one day
Reference 37
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Reference 38
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Reference 39
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Retrieval Augmented Recipe Generation Evaluating object hallucination in large vision-language models
Reference 40
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Reference 41
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Retrieval Augmented Recipe Generation Food and ingredient joint learning for fine- grained recognition
Reference 42
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Retrieval Augmented Recipe Generation Generating Personalized Recipes from Historical User Preferences
Reference 46
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Retrieval Augmented Recipe Generation Wide-slice residual networks for food recognition
Reference 47
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Reference 48
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Retrieval Augmented Recipe Generation A survey on food computing.ACM Computing Surveys (CSUR), 52(5):1–36, 2019
Reference 49
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Reference 53
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Retrieval Augmented Recipe Generation Learning multi-subset of classes for fine-grained food recognition
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Reference 55
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Reference 58
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Reference 59
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Reference 65
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Reference 66
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Reference 67
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Reference 68
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Reference 69
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Reference 73
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Reference 74
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Reference 75
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Reference 76
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Reference 77
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Retrieval Augmented Recipe Generation Unresolved cited work
Reference 78
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Retrieval Augmented Recipe Generation award winning soft chocolate chip cookies,
Reference 79
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Reference 80
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Reference 81
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Reference 82
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Observation af22566f-cfc2-4f64-99a7-5a2cbdd44bdd · outbound
Retrieval Augmented Recipe Generation As shown in Figure 7, in the first case, it can be observed that the model predic- tion partly overlaps with the ground truth
Reference 83
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Observation 8231d833-fbf6-4a23-8875-e692317b43d1 · outbound
Retrieval Augmented Recipe Generation In the first case, ingredients like ‘greens’ and ‘tomato’, which are visually prominent in the image, are successfully predicted
Reference 84
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Observation 38332879-f606-4844-9953-9805beb8ed3c · outbound
Retrieval Augmented Recipe Generation mango shrimp
Reference 85
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Observation 22fac13f-12c2-479a-a0cf-2d5c391dcf27 · outbound
Retrieval Augmented Recipe Generation butternut squash salad with hazelnuts
Reference 86
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Observation a7551f70-9a70-430e-b70c-c6100be7182e · inbound
Retrieval Augmented Recipe Generation Retrieval Augmented Recipe Generation
Reference 80
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Observation 13a60636-4db8-49d2-8ac2-f0913aa5ec59 · inbound
Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study Retrieval Augmented Recipe Generation
Reference 13
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