Multimodal Emotion Recognition with Large Language Models

Daiqing Wu; Hongrui Zhang; Kuien Liu; Sicheng Zhao; Yangyang Li; Yuhui Wang; Yu Zhou

arxiv: 2605.21239 · v1 · pith:NNS5RQTPnew · submitted 2026-05-20 · 💻 cs.MM

Multimodal Emotion Recognition with Large Language Models

Hongrui Zhang , Daiqing Wu , Yangyang Li , Kuien Liu , Yuhui Wang , Yu Zhou , Sicheng Zhao This is my paper

Pith reviewed 2026-05-21 01:07 UTC · model grok-4.3

classification 💻 cs.MM

keywords Multimodal Emotion RecognitionLarge Language ModelsAffective Data AugmentationMultimodal Affective RepresentationMultimodal Affective ReasoningEmotion InterpretationMER-with-LLMsReview

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The pith

This review organizes research on using large language models for multimodal emotion recognition into three directions based on how each addresses core challenges.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper identifies three primary challenges in the shift to large language models for recognizing emotions from combined inputs such as text, audio, and visuals: limited amounts of emotionally labeled data, mismatches in emotional signals within and between modalities, and difficulty in making the interpretation process transparent. It then groups existing studies according to whether they focus on expanding training data, building better joint representations of emotions across modalities, or improving the reasoning steps that turn those representations into emotion labels. A sympathetic reader would care because this structure turns scattered empirical experiments into a clearer landscape, showing where progress has occurred and where gaps remain for building systems that interpret emotions more like humans do in everyday settings.

Core claim

Prior works on multimodal emotion recognition with large language models can be categorized into three directions according to their focus on the challenges of data scarcity, affective gaps within and across modalities, and opacity of affective interpretation: Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning. Tracing development, trends, and open issues within each direction supplies a clear academic map of the MER-with-LLMs paradigm.

What carries the argument

The three-direction categorization (Affective Data Augmentation, Multimodal Affective Representation, Multimodal Affective Reasoning) that groups studies by the specific challenge each one targets.

Load-bearing premise

The primary challenges are scarcity of emotionally annotated data, affective gaps within and across modalities, and opacity of affective interpretation, and that existing works fit comprehensively into the three named directions.

What would settle it

A collection of recent papers on MER with LLMs whose methods address none of the three listed challenges or cannot be placed in any of the three categories without overlap or omission.

read the original abstract

Multimodal Emotion Recognition (MER) focuses on identifying and interpreting emotions from modality-compound inputs. Closely mirroring human cognitive processes in real-world environments, MER has drawn substantial attention from both academia and industry. Recently, a paradigm shift has been unveiled in MER, from leveraging small-scale, task-specific models to Large Language Models (LLMs). We refer to the latter as the MER-with-LLMs paradigm, which offers unprecedented generality, spurring numerous empirical attempts, even alongside speculation about LLMs' potential to achieve general emotional intelligence. However, with these new opportunities come new challenges, including the scarcity of emotionally annotated data, the affective gap both within and across modalities, and the opacity of affective interpretation. To systematically review existing research and guide future exploration, this paper categorizes prior works according to their focus on addressing these challenges into three directions: Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning. By thoroughly tracing the development, emerging trends, and remaining issues within each direction, this paper aims to provide a clear academic map of the MER-with-LLMs paradigm and foster its structured advancement.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a survey that groups MER-with-LLMs papers into three challenge-based directions but offers no new methods or data, so its usefulness depends entirely on whether the full taxonomy is complete and accurate.

read the letter

The main thing to know is that this is a survey paper offering a three-direction categorization of work on multimodal emotion recognition with large language models, based on challenges like data scarcity, cross-modal affective gaps, and opaque interpretation. It doesn't add new techniques or results but aims to map the field. What the paper does well is to clearly name the shift toward LLMs in this area and link specific research directions to those challenges. Organizing around Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning gives a simple framework that could help guide where future efforts might focus, especially for people trying to navigate the growing number of empirical studies. The soft spots are that we only have the abstract, so there's no way to verify how complete or accurate the grouping is. The abstract states the taxonomy but provides no examples of papers in each bucket, no count of how many works were reviewed, and no discussion of why these challenges are the primary ones or how other possible groupings were considered. This leaves open the possibility of overlap between categories or missed papers that don't fit neatly. The stress-test concern about taxonomy completeness holds here because nothing in the abstract lets us check for forced fits or unaddressed issues like prompt engineering or evaluation protocols. This paper is for researchers in affective computing and multimodal machine learning who are looking for a structured overview of the MER-with-LLMs paradigm. Someone entering the area or planning a project could get value from the high-level map and the tracing of trends, provided the full version has good coverage and fair summaries of prior work. I recommend sending this to peer review. A survey that successfully organizes a new subfield can be useful even without original contributions, and referees can assess the taxonomy's robustness and suggest improvements if needed.

Referee Report

1 major / 0 minor

Summary. The paper is a survey on the emerging MER-with-LLMs paradigm in multimodal emotion recognition. It identifies three challenges (scarcity of emotionally annotated data, affective gaps within/across modalities, and opacity of affective interpretation) and categorizes prior works addressing them into three directions—Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning—with the goal of tracing developments, trends, and issues to provide a clear academic map and guide future research.

Significance. If the taxonomy is demonstrated to be comprehensive, non-overlapping, and well-justified with concrete literature mappings, the survey could meaningfully organize a fast-growing area, highlight actionable trends within each direction, and support more structured progress toward general emotional intelligence with LLMs.

major comments (1)

Abstract: The central claim that categorizing works into Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning yields a clear academic map is load-bearing, yet the abstract provides no examples of papers assigned to each category, no coverage statistics, and no justification that the three directions are exhaustive or subsume alternatives such as prompt engineering or evaluation protocols. This prevents assessment of completeness or forced fits.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive comments on our survey of the MER-with-LLMs paradigm. We address the major comment point by point below and outline revisions to improve the abstract while preserving the paper's core structure and contributions.

read point-by-point responses

Referee: Abstract: The central claim that categorizing works into Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning yields a clear academic map is load-bearing, yet the abstract provides no examples of papers assigned to each category, no coverage statistics, and no justification that the three directions are exhaustive or subsume alternatives such as prompt engineering or evaluation protocols. This prevents assessment of completeness or forced fits.

Authors: We agree that the abstract's brevity limits immediate assessment of the taxonomy and will revise it to strengthen the central claim. The full manuscript systematically maps recent literature to the three directions based on how each work primarily addresses one of the three challenges (data scarcity, affective gaps, and interpretation opacity). In the revision, we will add one representative example per category (e.g., LLM-generated emotional dialogue synthesis for data augmentation, multimodal fusion adapters for representation, and chain-of-thought affective inference for reasoning) along with a concise justification that the directions derive directly from the challenges rather than arbitrary partitioning. Prompt engineering and similar techniques are typically integrated into the reasoning direction in the surveyed works, while evaluation protocols are discussed as cross-cutting issues rather than a fourth category. We will also note the survey's scope (recent works from 2022 onward) to indicate coverage. These targeted additions will make the taxonomy's rationale clearer without expanding the abstract beyond reasonable length. revision: yes

Circularity Check

0 steps flagged

No circularity: survey taxonomy organizes external literature without self-referential derivations

full rationale

The paper is a survey that identifies three challenges in MER-with-LLMs and proposes to categorize prior works into three corresponding directions. The abstract supplies no equations, predictions, fitted parameters, or derivations. The categorization is presented as an organizational map drawn from external research rather than a result derived from or equivalent to the paper's own inputs. No self-citations appear in the provided text, and the structure does not reduce to a self-definition or fitted input renamed as a prediction. This is a standard survey framing with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The review rests on domain assumptions about the state of the MER field and the centrality of the three listed challenges; no free parameters or invented entities are introduced.

axioms (1)

domain assumption A paradigm shift has occurred in multimodal emotion recognition from small-scale task-specific models to large language models.
Explicitly stated in the abstract as a recent development.

pith-pipeline@v0.9.0 · 5711 in / 1209 out tokens · 52845 ms · 2026-05-21T01:07:56.915282+00:00 · methodology

discussion (0)

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Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

categorizes prior works according to their focus on addressing these challenges into three directions: Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning

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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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