Papers by Hieu Trung Nguyen
Task-driven Layerwise Additive Activation Intervention (2025.naacl-short)
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| Challenge: | Existing approaches to task adaptation rely heavily on heuristic rules or prompt inputs. |
| Approach: | They propose a layer-wise additive activation intervention framework that steers the LMs’ generation process by identifying and manipulating the activations. |
| Outcome: | The proposed framework improves the accuracy of pretrained LMs and competing baselines on various datasets, demonstrating improvements in the accuracy and sample efficiency of the proposed framework. |
Mixture-of-Personas Language Models for Population Simulation (2025.findings-acl)
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Ngoc Bui, Hieu Trung Nguyen, Shantanu Kumar, Julian Theodore, Weikang Qiu, Viet Anh Nguyen, Rex Ying
| Challenge: | Pretrained LLMs fail to capture behavioral diversity of target populations due to inherent variability across individuals and groups. |
| Approach: | They propose a probabilistic prompting method that aligns LLM responses with the target population. |
| Outcome: | Experiments show that the proposed method outperforms competing methods in alignment and diversity metrics. |
Structured Pruning for Diverse Best-of-N Reasoning Optimization (2025.findings-acl)
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| Challenge: | Extensive experiments on the MATH dataset demonstrate that our method significantly outperforms traditional best-of-N and random head selection strategies. |
| Approach: | They propose a contrastive learning framework that dynamically selects the optimal head and layer to prune during inference by aligning question embeddings with head embedds. |
| Outcome: | The proposed approach outperforms best-of-N and random head selection strategies on the MATH500 and GSM8K datasets. |