Papers by Nam Le Hai
On the Impacts of Contexts on Repository-Level Code Generation (2025.findings-naacl)
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| Challenge: | CodeLLMs are widely used for code generation, but their ability to handle repository-level dependencies remains underexplored. |
| Approach: | They propose a benchmark for evaluating repository-level code generation based on dependency contexts. |
| Outcome: | The proposed model improves dependency handling and introduces a new metric, Dependency Invocation Rate (DIR), to measure context utilization. |
Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains (2025.naacl-short)
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Toan Ngoc Nguyen, Nam Le Hai, Nguyen Doan Hieu, Dai An Nguyen, Linh Ngo Van, Thien Huu Nguyen, Sang Dinh
| Challenge: | Existing document retrieval systems focus on a single language, targeting resource-rich languages like English or Chinese. |
| Approach: | They propose auxiliary loss function and symmetrical training strategy for cross-lingual retrieval between Vietnamese and English . they propose a dataset that covers the general domain and extends to the legal field . |
| Outcome: | The proposed dataset significantly improves state-of-the-art models on cross-lingual retrieval tasks. |
Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction (2025.naacl-long)
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Anh Duc Le, Nam Le Hai, Thanh Xuan Nguyen, Linh Ngo Van, Nguyen Thi Ngoc Diep, Sang Dinh, Thien Huu Nguyen
| Challenge: | Existing methods for relation extraction (RE) fail to address the problem of similar relations, which contributes to catastrophic forgetting. |
| Approach: | They propose a relation extraction method that utilizes relation descriptions and dynamic clustering to identify similar relations. |
| Outcome: | The proposed method mitigates catastrophic forgetting and outperforms state-of-the-art methods by a large margin. |
MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents (2026.findings-acl)
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Hung Pham Van, Nguyen Manh Hieu, Khang Pham Tran Tuan, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Trung Le
| Challenge: | Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. |
| Approach: | They propose a framework that integrates memory organization and retrieval via a Graph Intelligence framework. |
| Outcome: | Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation. |
Mitigating Non-Representative Prototypes and Representation Bias in Few-Shot Continual Relation Extraction (2025.acl-long)
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| Challenge: | Existing methods for few-shot continual relation extraction (FCRE) face two main challenges: non-representative prototypes and representation bias. |
| Approach: | They propose to use General Orthogonal Frame to create robust class prototypes . they also utilize label description representations as global class representatives . |
| Outcome: | The proposed method outperforms state-of-the-art methods on well-known benchmarks on well known FCRE benchmarks. |
MaGiX: A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual RAG (2025.findings-emnlp)
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Nguyen Manh Hieu, Vu Lam Anh, Hung Pham Van, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Thien Huu Nguyen
| Challenge: | Recent advances in Graph-based RAG (GRAG) frameworks focus on knowledge graphs for cross-lingual retrieval. |
| Approach: | They propose a new GRAG framework for cross-lingual question answering . MaGiX constructs a multi-granular cross-linguistic knowledge graph using fine-grained attribute descriptions and cross-synonym edges. |
| Outcome: | The proposed framework outperforms prior GRAG systems in retrieval accuracy and generation quality. |