SRCB at SemEval-2026 Task 5 A Multi-Target Finetuning Framework for Large Language Models with Joint Regression and Text Generation

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

SRCB developed a multi-target fine-tuning framework for decoder-only large language models, securing first place in SemEval-2026 Task 5. This task involved rating the plausibility of word senses within ambiguous stories, requiring the prediction of continuous plausibility scores that reflect human variability, rather than selecting a single correct sense. The proposed framework uniquely combines regression for precise score prediction with text generation, enabling the system to provide interpretable explanations for its plausibility ratings. To enhance robustness against inherent inter-annotator variability in human judgments, the framework incorporates specific objective-level strategies. This unified regressive–generative modeling approach proved highly effective for fine-grained plausibility estimation, demonstrating its superior performance in the competition.

Key takeaway

For NLP Engineers developing LLM systems that require nuanced human judgment, consider adopting a multi-target fine-tuning framework. You should jointly optimize regression for precise scoring and text generation for interpretable explanations, especially when dealing with tasks like plausibility estimation where continuous scores are needed. Incorporating objective-level strategies will enhance your system's robustness against inter-annotator variability, leading to more reliable and explainable predictions.

Key insights

Unified regressive–generative modeling enhances fine-grained plausibility estimation in large language models.

Principles

• Jointly optimize regression and text generation.
• Address inter-annotator variability for robustness.
• Predict continuous scores for human variability.

Method

A multi-target fine-tuning framework for decoder-only LLMs jointly optimizes regression for score prediction and text generation for explanations, incorporating objective-level strategies for robustness.

In practice

• Implement joint regression and generation for nuanced scoring.
• Apply objective-level strategies for variable human data.
• Fine-tune decoder-only LLMs for specific tasks.

Topics

• Large Language Models
• Fine-tuning
• Regression
• Text Generation
• Word Sense Disambiguation
• SemEval-2026

Best for: Research Scientist, AI Scientist, NLP Engineer

Related on AIssential

• The Hardest Part of Fine-Tuning Isn’t the Training — The most challenging aspect of NLP fine-tuning is effective label design and meticulous data annotation, not the model training itself.
• A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction — The framework decomposes pre-hoc fine-tuning prediction risk into intrinsic and reducible components, revealing fundamental uncertainty limits.
• Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models — Aligning task requirements with pre-training objectives significantly boosts encoder-decoder PLM performance.
• GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning — GRASS adaptively samples LLM layers for fine-tuning based on gradient norms, significantly reducing memory while boosting performance.
• Ines Montani on Natural Language Processing — Effective NLP problem-solving involves structured pipelines, precise data labeling, model fine-tuning, and leveraging LLMs for efficiency.
• Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation — Fine-tuned 8B-parameter language models can objectively forecast research idea success by comparing empirical outcomes, outperforming larger frontier models.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.