Paper Citation Record · LEDGER

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions

As of 15 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2411.15408.

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

pith.paper-citation-record.v1
2411.15408 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:22:28.098765Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T13:51:40.334057Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T13:55:53.240168Z

Reference resolution

43 of 43 outbound references displayed

verified exact2
verified fuzzy0
unresolved39
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 166bf934-6cb1-4c01-bb29-b870b45c0e4b · outbound

This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 2

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Observation 0b8ad43c-82d1-4a32-ba49-4727e3ddd24d · outbound

This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 3

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Source-reported events for the cited work

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Source-reported events for the cited work

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation 5089952e-8c39-467c-9835-c9830c00602b · outbound

This paper cites Shamim Hossain, Min Chen, Abdulhameed Alelaiwi, and Muneer Al-Hammadi.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Shamim Hossain, Min Chen, Abdulhameed Alelaiwi, and Muneer Al-Hammadi

Reference 9

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Observation 362d65ac-cde2-4c99-adf1-05bdca25a32c · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 10

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Source-reported events for the cited work

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 11

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 12

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions A Survey on Text Classification: From Shallow to Deep Learning

Reference 13

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 15

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This paper cites an unresolved cited work.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 16

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 18

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Observation adaa5728-3d01-4c1b-9d66-d948edffff1c · outbound

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Reference 19

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions GPT-4 Technical Report

Reference 20

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Observation f4bf536d-55c8-4e6c-8caa-267bcb00241a · outbound

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Generative Multimodal Entity Linking

Reference 23

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 26

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Reference 28

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Finetuned Language Models Are Zero-Shot Learners

Reference 30

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Benchmarking Machine Translation with Cultural Awareness

Reference 33

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Contextual Object Detection with Multimodal Large Language Models

Reference 35

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Observation 3e015be5-2525-47b4-bf90-b583c0dcea11 · outbound

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

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Reference 40

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Observation bc8f14b8-30ff-4b61-8aa8-fdea6ffef5ca · outbound

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions Unresolved cited work

Reference 41

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Source-reported events for the cited work

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This paper cites AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis.

Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis

Reference 45

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions ACM Computing Surveys (CsUR) 51, 6 (2019), 1–36

Reference 2019

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Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions IEEE Transactions on Circuits and Systems for Video Technology 33, 7 (2023), 3214–3228

Reference 2023

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Reference 3332

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8edcae6a-e8f2-4f26-ab9d-20c1edeab8ae · inbound

C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis cites this paper.

C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions

Reference 6

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Source-reported events for the cited work

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