Q-Scorer: Score Token and Decoder Paradigm for Multi-modal Large Language Model Scoring Optimization

Q-Scorer is a research project optimized for the scoring tasks of multi-modal large language models (MLLMs). It proposes an innovative "Score Token + Decoder" paradigm to address the shortcomings of current MLLMs in scoring tasks. This paradigm reframes the scoring task as a generation problem, applicable to various scenarios such as image quality assessment, video content scoring, and multi-modal alignment evaluation, providing new ideas for enhancing MLLM's scoring capabilities.

Multi-modal large language models have made significant progress in tasks like image understanding and visual question answering, but their performance in scoring tasks that output continuous values or discrete scores needs improvement. Traditional methods often treat scoring as a classification/regression problem, while Q-Scorer explores solutions that are more aligned with the nature of LLMs.

Score Token Mechanism

Introduce a dedicated "Score Token" as part of the vocabulary, corresponding to specific scores/intervals. Its advantages include:

• Discretizes the continuous score space
• The model's probability distribution can be interpreted as the confidence level of the score
• Extensible to different scoring ranges and granularities

Decoder Architecture Optimization

Adjust the decoder for scoring tasks:

• Restricted decoding space (limiting the range of score tokens)
• Structured output (ensuring format order)
• Confidence estimation (providing uncertainty via token probabilities)

Tasks Applicable to the Unified Scoring Paradigm

• Image quality assessment (clarity, composition, etc.)
• Video content scoring (quality, coherence, etc.)
• Multi-modal content alignment evaluation (matching degree between text and image/video)
• User preference prediction (personalized recommendation)

Application Scenarios

• Content platform quality assessment (assisting moderation/recommendation)
• Generative model evaluation (automatic feedback in AIGC scenarios)
• Education field (automatic evaluation of multimedia assignments)
• Scientific research data screening (quickly filtering high-quality samples)

Training Strategy

1. Pre-training: Learn visual-language alignment with large-scale multi-modal data
2. Score Token adaptation: Learn the correspondence between tokens and numerical values
3. Task fine-tuning: Optimize for specific scoring tasks

Loss Functions

• Token prediction loss (cross-entropy)
• Ranking loss (ensure score order aligns with real preferences)
• Calibration loss (align confidence with accuracy)

Inference Optimization

• Point estimation: Output the value corresponding to the most likely score token
• Distribution output: Return the complete score probability distribution
• Sampling output: Sample multiple scores from the distribution to support ensemble prediction

Aspect	Traditional Methods	Q-Scorer
Output Form	Direct regression or classification	Score token generation
Interpretability	Low (black-box prediction)	High (token probability)
Uncertainty Estimation	Usually not provided	Natively supported
Flexibility	Fixed scoring range	Extensible token design
Consistency with LLM Paradigm	Low	High

Current Limitations

1. Dataset dependency: Scoring tasks highly rely on the quality and scale of annotated data
2. Domain generalization: Generalization ability across different domains (e.g., medical images vs. natural images) needs verification
3. Fine-grained scoring: The granularity of discrete tokens may limit tasks requiring fine distinctions

Future Directions

• Explore more fine-grained score token designs
• Research few-shot/zero-shot scoring capabilities
• Expand to more modalities (audio, 3D content)
• Develop domain-specific scoring models

Q-Scorer is an innovative exploration of MLLM scoring tasks. By reframing scoring as a generation problem, it demonstrates how to use the generation capabilities of LLMs to solve traditional tasks. Its score token + decoder paradigm not only provides a technical solution but also reveals that when migrating traditional tasks to LLMs, we need to consider the inherent characteristics of the model. As multi-modal AI applications expand, high-quality automatic scoring capabilities will become more important, and Q-Scorer provides valuable references for this field.