Paper Citation Record · LEDGER

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback

As of 17 August 2026, this Paper Citation Record lists 100 of 148 outbound references and 12 inbound Pith citation observations for arXiv:2412.15838.

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

pith.paper-citation-record.v1
2412.15838 v2

Coverage vector

measured 100 of 148 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:09:13.456124Z

measured 112 of 112 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:17:48.396266Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T01:32:22.464378Z

Reference resolution

100 of 148 outbound references displayed

verified exact0
verified fuzzy0
unresolved100
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e81517b-58b7-46cb-bd5b-b1732ee3f832 · outbound

This paper cites GPT-4 Technical Report.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.852944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d52a87ef-a321-4f93-845b-1826919ccf25 · outbound

This paper cites MusicLM: Generating Music From Text.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback MusicLM: Generating Music From Text

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.859946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bac8d475-881c-409c-a107-3ae2a2ef5b20 · outbound

This paper cites Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.868191Z

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Observation fd8555b2-c7c2-4670-ac18-d9c31f2016e3 · outbound

This paper cites Audio visual scene- aware dialog.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Audio visual scene- aware dialog

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.874672Z

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Unavailable: canonical work link unavailable.

Observation 1503e659-c43e-4fd1-901d-6540d2303c1a · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Flamingo: a visual language model for few-shot learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.881406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2663749-bb91-47aa-9c7e-0f672f63ae3b · outbound

This paper cites Understanding Alignment in Multimodal LLMs: A Comprehensive Study.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Understanding Alignment in Multimodal LLMs: A Comprehensive Study

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.888377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c6d5649-96cb-442c-9911-4100989e731e · outbound

This paper cites Claude 3.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Claude 3

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.895831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de26c618-7e77-47b9-b8c4-c38592d6a3e8 · outbound

This paper cites Pika art.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Pika art

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.901531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09007dbb-32d6-4376-bb9e-42bb9c44066c · outbound

This paper cites Qwen Technical Report.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Qwen Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.908026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91a851fe-83d9-46b3-90fc-ecc76891fbfb · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Constitutional AI: Harmlessness from AI Feedback

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.913726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9a9a1cc-13cc-42ea-a12d-61609ab083bf · outbound

This paper cites Training diffusion models with reinforce- ment learning.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Training diffusion models with reinforce- ment learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.919821Z

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Unavailable: canonical work link unavailable.

Observation b31dba60-d8e6-4584-9d42-24d676fe3243 · outbound

This paper cites Activitynet: A large-scale video benchmark for human activity understanding.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Activitynet: A large-scale video benchmark for human activity understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.925690Z

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Observation c1a21545-2c5e-419e-929b-ef44887ff30c · outbound

This paper cites GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.931762Z

Source-reported events for the cited work

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Observation 38661ef8-7c50-4580-8710-fa8e250bcd0e · outbound

This paper cites Vggsound: A large-scale audio-visual dataset.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Vggsound: A large-scale audio-visual dataset

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.937011Z

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Observation 60f55d3b-1bb1-406d-8821-88539e848548 · outbound

This paper cites ShareGPT4V: Improving Large Multi-Modal Models with Better Captions.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.942401Z

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Observation 88c47207-6c7b-49ab-bc00-ebfba7a9d09e · outbound

This paper cites ShareGPT4Video: Improving Video Understanding and Generation with Better Captions.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback ShareGPT4Video: Improving Video Understanding and Generation with Better Captions

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.947500Z

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Observation 247aed36-34ab-473b-9817-39513e3658e6 · outbound

This paper cites VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.953832Z

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Observation 45e7e75a-7fdc-4bdb-a770-977b127b493a · outbound

This paper cites Vast: A vision- audio-subtitle-text omni-modality foundation model and dataset.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Vast: A vision- audio-subtitle-text omni-modality foundation model and dataset

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.959129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed598795-6dd7-43e4-b9f9-770376e7ef9c · outbound

This paper cites Panda-70m: Captioning 70m videos with multiple cross-modality teachers.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Panda-70m: Captioning 70m videos with multiple cross-modality teachers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.964448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2ea859f-d959-4b56-935c-af0e1f21295d · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.969123Z

Source-reported events for the cited work

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Observation f8336829-5615-42f6-9e6f-b014cd58f082 · outbound

This paper cites Mitigating Hallucination in Visual Language Models with Visual Supervision.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Mitigating Hallucination in Visual Language Models with Visual Supervision

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.974082Z

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Observation d6d10d20-c643-4d77-9942-832649580bad · outbound

This paper cites ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.980482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07f4a4f6-c6c2-44d8-a12f-ec79be421720 · outbound

This paper cites Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.986477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a00611e-49c7-4d9a-9719-ca4cbf45eab8 · outbound

This paper cites Qwen2-Audio Technical Report.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Qwen2-Audio Technical Report

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:12.998854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa7b9795-b534-44f9-b4b7-bf9001eb97e6 · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:13.005404Z

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Observation 1e6fc323-5bdd-4293-8318-25237c8ff182 · outbound

This paper cites SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:13.011046Z

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Observation 0d122bb7-bdbb-4cb4-8b9c-1ce52a23bfa3 · outbound

This paper cites Cogview2: Faster and better text-to-image generation via hierarchical transformers.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Cogview2: Faster and better text-to-image generation via hierarchical transformers

Reference 28

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Observation 5472f8e1-8f7e-45b1-8ce4-9313c5683e90 · outbound

This paper cites En- hancing chat language models by scaling high-quality in- structional conversations.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback En- hancing chat language models by scaling high-quality in- structional conversations

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:13.023864Z

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Observation f3205791-f2ca-4b91-bbd0-d17997e0b8df · outbound

This paper cites An im- age is worth 16x16 words: Transformers for image recog- nition at scale, 2021.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback An im- age is worth 16x16 words: Transformers for image recog- nition at scale, 2021

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T11:09:13.030121Z

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Observation 656d4e90-b8a3-4bac-8946-e8d20df43bf0 · outbound

This paper cites The Llama 3 Herd of Models.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback The Llama 3 Herd of Models

Reference 31

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Observation 2a5529be-a93c-4d83-835e-5ddc9c3608f0 · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback KTO: Model Alignment as Prospect Theoretic Optimization

Reference 32

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Observation 690c0005-3455-4214-9ef2-dddab68c9748 · outbound

This paper cites Stable Audio Open.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Stable Audio Open

Reference 33

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Observation 4ea0a9ce-51d8-465f-bc6c-460ce4fd60b6 · outbound

This paper cites LLaMA-Omni: Seamless Speech Interaction with Large Language Models.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback LLaMA-Omni: Seamless Speech Interaction with Large Language Models

Reference 34

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Observation b536adab-cfed-43a9-bfe4-191f80c54efd · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 35

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Observation 85f84202-71d9-425c-bf37-bd33c12ac764 · outbound

This paper cites Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 36

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Observation 63d74bcb-37a2-4a4c-ac6c-b893ed47c36f · outbound

This paper cites Make-a-scene: Scene- based text-to-image generation with human priors.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Make-a-scene: Scene- based text-to-image generation with human priors

Reference 37

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Observation 148dd874-f385-44f2-b83d-6e3e7103bb79 · outbound

This paper cites Imagebind: One embedding space to bind them all.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Imagebind: One embedding space to bind them all

Reference 38

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Observation 15418cca-6bf9-4945-ac03-c2774b6029c9 · outbound

This paper cites Listen, Think, and Understand.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Listen, Think, and Understand

Reference 39

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This paper cites Ego4d: Around the world in 3,000 hours of egocentric video.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Ego4d: Around the world in 3,000 hours of egocentric video

Reference 40

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Observation 0c8b9add-d6fd-45b3-8f30-5cdf73f7f249 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Denoising dif- fusion probabilistic models

Reference 41

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Observation d2eb6f4f-f26e-4607-acfa-6eb2ed2f802d · outbound

This paper cites Reference-free monolithic preference optimization with odds ratio.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Reference-free monolithic preference optimization with odds ratio

Reference 42

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Observation a7c67a3f-5bac-44b7-95f1-92b36799e431 · outbound

This paper cites Cogvideo: Large-scale pretraining for text-to- video generation via transformers.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Cogvideo: Large-scale pretraining for text-to- video generation via transformers

Reference 43

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This paper cites Tifa: Accurate and interpretable text-to-image faithfulness eval- uation with question answering.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Tifa: Accurate and interpretable text-to-image faithfulness eval- uation with question answering

Reference 44

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Observation 539d44e8-d27c-4b28-bdc9-dc4624d101e9 · outbound

This paper cites Movienet: A holistic dataset for movie under- standing.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Movienet: A holistic dataset for movie under- standing

Reference 45

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Observation 58290bbe-8e3e-418e-a569-0aba224d9d9a · outbound

This paper cites The Platonic Representation Hypothesis.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback The Platonic Representation Hypothesis

Reference 46

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Observation f2ca3893-4af5-4b83-98ae-806bcfb036ec · outbound

This paper cites AI Alignment: A Comprehensive Survey.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback AI Alignment: A Comprehensive Survey

Reference 47

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference

Reference 48

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback FollowBench: A multi-level fine-grained constraints following benchmark for large language mod- els

Reference 49

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Observation 54d42660-f8c6-4c10-84e8-6790d5ddcc56 · outbound

This paper cites FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models

Reference 50

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This paper cites Audiocaps: Generating captions for audios in the wild.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Audiocaps: Generating captions for audios in the wild

Reference 51

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This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 52

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback black-forest-labs/flux (github reposi- tory)

Reference 53

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Observation fbe258e0-7c4a-4e17-a62c-f9692fc2191a · outbound

This paper cites Learning to answer questions in dynamic audio-visual scenarios.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Learning to answer questions in dynamic audio-visual scenarios

Reference 54

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This paper cites Mvbench: A comprehensive multi-modal video under- standing benchmark.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Mvbench: A comprehensive multi-modal video under- standing benchmark

Reference 55

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This paper cites Vlfeedback: A large-scale ai feedback dataset for large vision-language models alignment.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Vlfeedback: A large-scale ai feedback dataset for large vision-language models alignment

Reference 56

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Observation c589e657-ea0b-4c5d-894e-81d0c94b7e3d · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Evaluating Object Hallucination in Large Vision-Language Models

Reference 57

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Rich human feedback for text-to-image generation

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Improved baselines with visual instruction tuning

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

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Observation 6faa12d4-3d54-4025-8229-7f2bd9c1f394 · outbound

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Visual instruction tuning

Reference 65

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Audioldm 2: Learning holistic audio generation with self-supervised pretraining

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Video-chatgpt: Towards detailed video understanding via large vision and language models

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Deepart: Learning joint representations of visual arts

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback SimPO: Simple Preference Optimization with a Reference-Free Reward

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Unresolved cited work

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Training lan- guage models to follow instructions with human feedback

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Sdxl: Improving latent diffusion models for high-resolution image synthesis

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Learn- ing transferable visual models from natural language super- vision

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Observation d6c87cb7-6535-44b1-b4d7-8671b2ca5335 · outbound

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Robust speech recognition via large-scale weak supervision

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Observation 2f43c59d-282b-4b0c-9a2f-abdaf43a0c5b · outbound

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Direct preference optimization: Your language model is secretly a reward model

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback High-resolution image synthesis with latent diffusion models

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Laion-5b: An open large-scale dataset for train- ing next generation image-text models

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback A simple baseline for audio-visual scene-aware dialog

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback A-okvqa: A benchmark for visual question answering using world knowledge

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Generative multimodal models are in-context learners

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Aligning Large Multimodal Models with Factually Augmented RLHF

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Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback Stanford alpaca: An instruction-following llama model, 2023

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Observation fc88937f-7680-458d-8545-f77f7945806f · inbound

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