Papers by Viet Thanh Pham
Distributional Alignment for Large Language Models under Domain Shift (2026.findings-acl)
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| Challenge: | Existing distributional alignment models are unstable and degrade under cultural and domain shifts. |
| Approach: | They propose a distributional alignment technique that improves distribution prediction under cultural and domain shift. |
| Outcome: | The proposed method improves fidelity and robustness of LLM distribution estimation under domain and cultural shift. |
SurveyPilot: an Agentic Framework for Automated Human Opinion Collection from Social Media (2025.acl-long)
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| Challenge: | Existing methods for opinion survey research exhibit severe biases and lack traceability. |
| Approach: | They propose a finite-state orchestrated agentic framework that automates the collection and analysis of human opinions from social media platforms. |
| Outcome: | The proposed framework achieves close alignment with authentic survey results across multiple domains, with average relative improvements of 68,98% and 51,37% when compared to opinion synthesis and agent-based approaches. |
LiveCultureBench: a Multi-Agent, Multi-Cultural Benchmark for Large Language Models in Dynamic Social Simulations (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly deployed as autonomous agents . evaluations focus primarily on task success rather than cultural appropriateness or reliability. |
| Approach: | They propose a multi-cultural, dynamic benchmark that embeds large language models as agents in a simulated town and evaluates them on task completion and adherence to socio-cultural norms. |
| Outcome: | The proposed model evaluates LLMs on task completion and adherence to socio-cultural norms across models and cultural profiles. |
Discourse Graph Guided Document Translation with Large Language Models (2026.eacl-long)
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| Challenge: | Recent agentic machine translation systems mitigate context window constraints but require substantial computational resources and are sensitive to memory retrieval strategies. |
| Approach: | They propose a framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than sequential or exhaustive context. |
| Outcome: | The proposed framework surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead. |
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model (2025.findings-acl)
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| Challenge: | Recent research has demonstrated that large language models (LLMs) can translate cultural elements in languages such as idioms and proverbs. |
| Approach: | They propose to use large language models to translate culturally rooted proverbs in conversation and between languages with similar cultural backgrounds to compare their results. |
| Outcome: | The proposed models can achieve good translation between languages with similar cultural backgrounds and outperform NMT models in proverb translation. |
CultureInstruct: Curating Multi-Cultural Instructions at Scale (2025.naacl-long)
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| Challenge: | Large language models exhibit severe cultural bias, despite their success in recent years . a critical challenge of LLMs is integration of cultural knowledge into these models . |
| Approach: | They propose a large-scale instruction-tuning dataset to reduce cultural bias in large language models. |
| Outcome: | The proposed model outperforms GPT-4o Mini and GPT-42 with 18.47% and 13.07% relative improvements on cultural benchmarks. |