Low-rank adaptation (LoRA) has been developed as an efficient approach for adapting large language models (LLMs) by fine-tuning two low-rank matrices, thereby reducing the number of trainable parameters. However, prior research indicates that many of the weights in these matrices are redundant, leading to inefficiencies in parameter utilization. To address this limitation, we introduce Dense Low-Rank Adaptation (DenseLoRA), a novel approach that enhances parameter efficiency while achieving superior performance compared to LoRA. DenseLoRA builds upon the concept of representation fine-tuning, incorporating a single Encoder-Decoder to refine and compress hidden representations across all adaptation layers before applying adaptation. Instead of relying on two redundant low-rank matrices as in LoRA, DenseLoRA adapts LLMs through a dense low-rank matrix, improving parameter utilization and adaptation efficiency. We evaluate DenseLoRA on various benchmarks, showing that it achieves 83.8% accuracy with only 0.01% of trainable parameters, compared to LoRA’s 80.8% accuracy with 0.70% of trainable parameters on LLaMA3-8B. Additionally, we conduct extensive experiments to systematically assess the impact of DenseLoRA’s components on overall model performance.

An increasing adoption of Large Language Models (LLMs) in complex reasoning tasks necessitates their interpretability and reliability. Recent advances to that end include retrieval-augmented generation (RAG) and knowledge graph-enhanced RAG (GraphRAG), whereas they are constrained by static knowledge bases and ineffective multimodal data integration. In response, we propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics. Our approach 1) derives graph patterns from query semantics to guide knowledge extraction, 2) employs a multi-path retrieval strategy to pinpoint core knowledge, and 3) supplements missing multimodal information ad hoc. Experimental results on the MultimodalQA and WebQA datasets demonstrate that our framework achieves the state-of-the-art performance among unsupervised competitors, particularly excelling in cross-modal understanding of complex queries.

Event causality identification (ECI) is a challenging task that involves predicting causal relationships between events in text. Existing prompt-learning-based methods typically concatenate in-context examples only at the input layer, this shallow integration limits the model’s ability to capture the abstract semantic cues necessary for identifying complex causal relationships. To address this limitation, we propose a novel model called Deep In-Context Prompt (DICP), which injects in-context examples into the deeper layer of a pre-trained language model (PLM). This strategy enables the model to leverage the hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions. Moreover, DICP introduces a multi-layer prompt injection mechanism, distributing diverse in-context examples across multiple transformer layers. This design allows the model to recognize a broader range of causal patterns and improves its generalization across different contexts. We evaluate the DICP model through extensive experiments on two widely used datasets, demonstrating its significant improvement in ECI performance compared to existing approaches. Furthermore, we explore the impact of varying the number of deep layers on performance, providing valuable insights into the optimal layer configuration for ECI tasks.