Papers with lightweight fine-tuning
Toward Inclusive Language Models: Sparsity-Driven Calibration for Systematic and Interpretable Mitigation of Social Biases in LLMs (2025.findings-emnlp)
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| Challenge: | a new method to mitigate stereotypical bias in large language models is needed . inherent biases from training on vast Internet datasets can amplify harmful stereotypes . |
| Approach: | They propose a method to identify stereotypical bias in decoder-only transformer models . they apply a localization mechanism that correlates internal activations with a new Context Influence score . |
| Outcome: | The proposed method reduces stereotypical biases on BBQ, StereoSet, and CrowS-Pairs while improving reasoning performance on MMLU by 10%. |
Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026.acl-long)
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Zehua Pei, Hui-Ling Zhen, Lancheng Zou, Xianzhi Yu, Wulong Liu, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu
| Challenge: | Large language models (LLMs) are fast but require expensive pre-training . a new approach to scale large language models into MoEs reduces inference costs . |
| Approach: | They propose an analytical post-training framework that rapidly restructures FFNs into sparse MoE architectures using only a small calibration dataset. |
| Outcome: | The proposed framework outperforms existing methods on a small calibration dataset. |
LVLM-Aware Multimodal Retrieval for RAG-Based Medical Diagnosis with General-Purpose Models (2026.findings-acl)
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| Challenge: | Using retrieval augmentation, large vision language models can be used for diagnostic accuracy, but multimodal retrieval-augmented diagnosis is challenging. |
| Approach: | They propose a lightweight mechanism for enhancing diagnostic performance of retrieval-augmented LVLMs by fine-tuning a multimodal retriever and general-purpose backbone models. |
| Outcome: | The proposed mechanism achieves competitive results without medical training compared to pre-trained models with extensive training. |
Enhancing Parameter-efficient Fine-tuning with Simple Calibration Based on Stable Rank (2024.lrec-main)
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| Challenge: | Existing methods for lightweight fine-tuning are ineffective in low-resource settings but fail in high-resourced settings, leading to unreliable outcomes. |
| Approach: | They propose a calibration strategy that takes into account the inherent variance of generalization ability in model components and potential changes during the fine-tuning process. |
| Outcome: | The proposed calibration improves GLUE score by 3.1 points over the previous calibration method. |
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion . |
| Approach: | They propose to use large language models to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning. |
| Outcome: | The proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task. |
Segment-Based Attention Masking for GPTs (2025.acl-long)
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| Challenge: | Causal masking is a fundamental component in Generative Pre-Trained Transformers (GPT) models, playing a crucial role during training. |
| Approach: | They propose to apply causal masking to all input tokens step-by-step, mimicking the generation process. |
| Outcome: | The proposed model can process the entire user prompt at once, but it is applied to all input tokens step-by-step, mimicking the generation process. |
FuseChat: Knowledge Fusion of Chat Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are costly and require significant computational resources and time. |
| Approach: | They propose a fuse-and-merge framework for the knowledge fusion of chat LLMs . they conduct pairwise knowledge fusing on source chat LRMs to create multiple target LLM . |
| Outcome: | The proposed framework is superior to baselines of various sizes. |