Papers by Hoang Pham
An adaptable task-oriented dialog system for stand-alone embedded devices (P19-3)
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Long Duong, Vu Cong Duy Hoang, Tuyen Quang Pham, Yu-Heng Hong, Vladislavs Dovgalecs, Guy Bashkansky, Jason Black, Andrew Bleeker, Serge Le Huitouze, Mark Johnson
| Challenge: | a proposed speech-based task-oriented dialogue system is built on a small embedded device . the system does not require internet connectivity because all components run locally on the device - a cost-effective solution . |
| Approach: | They propose a spoken-language end-to-end task-oriented dialogue system for small embedded devices such as home appliances. |
| Outcome: | The proposed system is based on a demo run offline on swiss raspberry pi . it eliminates privacy risks and eliminates server costs and latency . |
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference (2026.acl-long)
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Cuong Chi Le, Minh V.t. Pham, Tung D. Vu, Van Duc Cuong, Phan Nhat Huy, Phan Nhat Hoang, Tien N. Nguyen
| Challenge: | Existing methods for generating specifications are limited and often fail to infer semantic specifications such as pre-/postconditions. |
| Approach: | They propose a framework that treats LLMs as exploratory reasoners rather than one-shot generators. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in accuracy and completeness of generated postconditions. |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM (2025.findings-acl)
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| Challenge: | Existing verification methods rely on unstructured text corpora to break down claims . despite strong reasoning abilities, modern LLMs struggle with modular pipelines . |
| Approach: | They propose a framework that integrates knowledge graphs with LLM reasoning . they propose KGs provide structured, semantically rich representations . |
| Outcome: | The proposed framework outperforms baselines on the FactKG dataset by 9%-12% accuracy points across multiple categories. |
Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation (2025.naacl-long)
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| Challenge: | Existing approaches to claim verification are based on decomposing claims into sub-claims and querying a knowledge base to resolve hidden or ambiguous entities. |
| Approach: | They propose a framework that leverages the reasoning and comprehension abilities of LLM agents to solve ambiguous entities in a graph. |
| Outcome: | The proposed framework achieves competitive performance compared to baselines across benchmarks. |
Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances (2022.aacl-demo)
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| Challenge: | Existing systems to itemize meetings' decisions are lacking in their raw form due to utterance collapse. |
| Approach: | They propose a prototype system to construct decision items that deal with utterance collapse in natural conversation. |
| Outcome: | The proposed system improves the user experience by dealing with utterance collapse in natural conversation. |
VN-MTEB: Vietnamese Massive Text Embedding Benchmark (2026.findings-eacl)
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| Challenge: | a lack of large-scale test datasets makes it difficult to evaluate AI models before deploying them in real-world projects. |
| Approach: | They propose a Vietnamese benchmark for embedding models that leverages large language models and embeddable models to translate and filter samples from the Massive Multilingual Text Embedding Benchmark. |
| Outcome: | The proposed benchmark outperforms existing models in Vietnamese and English tasks with 41 datasets. |
Who’s Who: Large Language Models Meet Knowledge Conflicts in Practice (2024.findings-emnlp)
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| Challenge: | Recent large-scale pretrained language models excel in tasks requiring natural language understanding, but they often "hallucinate" plausible but incorrect content due to outdated or incorrect pretraining information. |
| Approach: | They propose a public benchmark dataset to examine model’s behavior in knowledge conflict situations. |
| Outcome: | The proposed model induces conflicts by asking about a common property among entities having the same name, resulting in questions with up to 8 distinctive answers. |
Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models (2026.acl-long)
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| Challenge: | Existing methods to quantize large language models suffer from significant accuracy loss at low bit-widths due to high-impact parameters. |
| Approach: | They propose a quadratic optimization framework that quantizes high-impact parameters to moderate bit-widths while quantizing low bit-wideths. |
| Outcome: | The proposed framework preserves high-impact parameters while preserving memory usage. |