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Junchen Li

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AAAI Conference 2025 Conference Paper

LRM-LLaVA: Overcoming the Modality Gap of Multilingual Large Language-Vision Model for Low-Resource Languages

  • Junchen Li
  • Qing Yang
  • Bojian Jiang
  • Shaolin Zhu
  • Qingxuan Sun

Multilingual large language-vision models (LVLMs), which understand and generate both text and images across multiple languages, have achieved remarkable performance on English-centric multimodal generation tasks. However, their performance on non-English tasks has been underwhelming. One major challenge with multilingual LVLMs is the modality gap between visual inputs and multilingual textual inputs/outputs due to the lack of high-quality multilingual training data. In this paper, we propose LRM-LLaVA, a multilingual large language-vision model designed for low-resource languages to overcome the modality gap. It is composed of four components: a visual encoder, a multilingual large language model, a vision-text representation projector, and a cross-modal regularizer. Both the projector and regularizer aim at reducing the modality gap and improving multilingual performance. To train LRM-LLaVA, we employ a two-stage training strategy including pre-training and instruction fine-tuning. Meanwhile, we construct a multilingual visual question answering dataset based on English open-source datasets and adopt multiple task instructions. To evaluate the performance of LVLMs across various languages, we construct four multilingual benchmarks for 10 languages, based on English open-source benchmarks. Experimental results show that LRM-LLaVA achieves competitive performance compared to other multilingual LVLMs of similar parameters.

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NeurIPS Conference 2025 Conference Paper

NeuroPath: Neurobiology-Inspired Path Tracking and Reflection for Semantically Coherent Retrieval

  • Junchen Li
  • Rongzheng Wang
  • Yihong Huang
  • Qizhi Chen
  • Jiasheng Zhang
  • Shuang Liang

Retrieval-augmented generation (RAG) greatly enhances large language models (LLMs) performance in knowledge-intensive tasks. However, naive RAG methods struggle with multi-hop question answering due to their limited capacity to capture complex dependencies across documents. Recent studies employ graph-based RAG to capture document connections. However, these approaches often result in a loss of semantic coherence and introduce irrelevant noise during node matching and subgraph construction. To address these limitations, we propose NeuroPath, an LLM-driven semantic path tracking RAG framework inspired by the path navigational planning of place cells in neurobiology. It consists of two steps: Dynamic Path Tracking and Post-retrieval Completion. Dynamic Path Tracking performs goal-directed semantic path tracking and pruning over the constructed knowledge graph (KG), improving noise reduction and semantic coherence. Post-retrieval Completion further reinforces these benefits by conducting second-stage retrieval using intermediate reasoning and the original query to refine the query goal and complete missing information in the reasoning path. NeuroPath surpasses current state-of-the-art baselines on three multi-hop QA datasets, achieving average improvements of 16. 3\% on recall@2 and 13. 5\% on recall@5 over advanced graph-based RAG methods. Moreover, compared to existing iter-based RAG methods, NeuroPath achieves higher accuracy and reduces token consumption by 22. 8\%. Finally, we demonstrate the robustness of NeuroPath across four smaller LLMs (Llama3. 1, GLM4, Mistral0. 3, and Gemma3), and further validate its scalability across tasks of varying complexity. Code is available at https: //github. com/KennyCaty/NeuroPath.

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