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Linh Ngo Van

Linh Ngo Van has 6 papers in our library, in 2025–2026, most of them on Language models & pretraining, Dialogue systems, Multilingual & cross-lingual NLP; often with Trung Le, Nguyen Thi Ngoc Diep, Dinh Viet Sang.

6 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 9 · 64 works · 337 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 27 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9115

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments (2025 · 10 citations)
    Large text embedding models such as LLM2Vec are expensive to deploy. Knowledge distillation can compress them, but it gets harder when the teacher and the student split text with different tokenizers. Earlier cross-tokenizer KD (CTKD) methods mostly match final outputs or logits. They ignore the relational knowledge…
  2. CTPD: Cross Tokenizer Preference Distillation (2026 · 8 citations)
    Small language models are cheaper to deploy but harder to align with human preferences. One remedy is to distill alignment from a larger, already-aligned teacher, so the expensive alignment step is paid only once. The richest form of this is white-box distillation, which uses the teacher's logits rather than its…
  3. Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models (2026 · 1 citation)
    Dynamic topic models track how themes in timestamped collections appear, change and fade. The authors identify three weaknesses in current approaches. Encoders learn only from bag-of-words counts and ignore knowledge in pre-trained language models. Topics in neighbouring time slices are usually linked one-to-one, so…
  4. LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models (2026)
    Cross-lingual topic models try to find themes shared across languages. They depend on limited bilingual resources such as seed dictionaries, document embeddings or parallel corpora, so paired topics often drift apart in meaning. Earlier LLM-assisted methods either query the model for every document, which is costly…
  5. MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents (2026)
    LLM-based conversational agents cannot remember users across long, multi-session interactions. Current graph-based memory stores also have problems: they keep too much generic chatter, they cannot show which dialogue turn a fact came from, and they rank evidence the same way regardless of the question. MemORAI…
  6. Octopus: Gated Selective Attention for Memory-Bounded Long-Context Inference in Large Language Models (2026)
    The KV cache of a Transformer grows linearly with context length, so long-context inference becomes memory-bound.

All works

7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

  1. Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains ↗Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) · no summary yetToan Ngoc Nguyen et al.2025 · 14 citations
  2. CTPD: Cross Tokenizer Preference DistillationarXiv:2601.11865Truong Nguyen, Phi Van Dat, Ngan Nguyen, et al.2026 · 8 citations

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 27 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

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