Current vision-language models (VLMs) understand complex vision-text tasks by extracting overall semantic information from large-scale cross-modal associations. However, extracting from large-scale cross-modal associations often smooths out semantic details and requires large computations, limiting multimodal fine-grained understanding performance and efficiency. To address this issue, this paper proposes a detail-oriented prompt learning (DoPL) method for vision-language models to implement fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters. According to the low-entropy information concentration theory, DoPL explores shared interest tokens from text-vision correlations and transforms them into alignment weights to enhance text prompt and vision prompt via detail-oriented prompt generation. It effectively guides the current frozen layer to extract fine-grained text-vision alignment cues. Furthermore, DoPL constructs detail-oriented prompt generation for each frozen layer to implement layer-by-layer localization of fine-grained semantic alignment, achieving precise understanding in complex vision-text tasks. DoPL performs well in parameter-efficient fine-grained semantic alignment with only 0.12% tunable parameters for vision-language models. The state-of-the-art results over the previous parameter-efficient fine-tuning methods and full fine-tuning approaches on six benchmarks demonstrate the effectiveness and efficiency of DoPL in complex multi-modal tasks.

Low-rank adaptation (LoRA) efficiently adapts LLMs to downstream tasks by decomposing LLMs’ weight update into trainable low-rank matrices for fine-tuning. However, the random low-rank matrices may introduce massive task-irrelevant information, while their recomposed form suffer from limited representation spaces under low-rank operations. Such dense and choked adaptation in LoRA impairs the adaptation performance of LLMs on downstream tasks. To address these challenges, this paper proposes OHoRA, an orthogonal high-rank adaptation for parameter-efficient fine-tuning on LLMs. According to the information bottleneck theory, OHoRA decomposes LLMs’ pre-trained weight matrices into orthogonal basis vectors via QR decomposition and splits them into two low-redundancy high-rank components to suppress task-irrelevant information. It then performs dynamic rank-elevated recomposition through Kronecker product to generate expansive task-tailored representation spaces, enabling precise LLM adaptation and enhanced generalization. OHoRA effectively operationalizes the information bottleneck theory to decompose LLMs’ weight matrices into low-redundancy high-rank components and recompose them in rank-elevated manner for more task-tailored representation spaces and precise LLM adaptation. Empirical evaluation shows OHoRA’s effectiveness by outperforming LoRA and its variants and achieving comparable performance to full fine-tuning with only 0.0371% trainable parameters.