Papers by Bao 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. |
NUMINA: A Natural Understanding Benchmark for Multi-dimensional Intelligence and Numerical Reasoning Abilities (2025.findings-emnlp)
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Changyu Zeng, Yifan Wang, Zimu Wang, Wei Wang, Zhengni Yang, Muyi Bao, Jimin Xiao, Anh Nguyen, Yutao Yue
| Challenge: | Existing 3D benchmarks lack fine-grained numerical reasoning task annotations, limiting MLLMs’ ability to perform precise spatial measurements and complex numerical reasoning. |
| Approach: | They propose a 3D-based benchmark to enhance indoor perceptual understanding by using multi-scale annotations and question-answer pairs. |
| Outcome: | The proposed benchmark improves indoor perceptual understanding by incorporating multi-scale annotations and question-answer pairs. |
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. |
Distributional Surgery for Language Model Activations (2025.findings-emnlp)
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| Challenge: | Language models can produce undesirable outputs including harmful or toxic outputs. |
| Approach: | They propose a method to detect undesirable content using activations . they propose layerwise distributional steering policies that transform the attention heads . |
| Outcome: | The proposed method outperforms baselines in reducing undesirable output generation. |
Probe-Free Low-Rank Activation Intervention (2025.naacl-long)
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| Challenge: | Existing activation intervention methods involve an activation probe to detect undesirable generation, triggering the activation modification to steer subsequent generation. |
| Approach: | They propose a probe-free intervention method FLORAIN for all attention heads in a specific activation layer. |
| Outcome: | The proposed method outperforms baseline methods in enhancing model truthfulness and quality across generation and multiple-choice tasks. |
Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking (2023.eacl-main)
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Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, Michael Witbrock
| Challenge: | Existing scientific fact-checking datasets are limited due to expertise bottleneck . multi2Claim pipeline is a tool to convert multiple-choice questions into fact- checking data . |
| Approach: | They propose a pipeline for automatically converting multiple-choice questions into fact-checking data . they generate two large-scale datasets for scientific-fact-checker tasks . success at this task can help the reader understand scientific topics and promote science . |
| Outcome: | The proposed pipeline improves performance on two large-scale scientific fact-checking datasets. |
Can Large Language Models Learn Independent Causal Mechanisms? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform poorly on complex reasoning tasks, such as abstract, causal, or logical reasoning. |
| Approach: | They propose to use two concepts from causality to learn ICMs within LLMs to improve out-of-distribution performance on abstract and causal reasoning tasks. |
| Outcome: | The proposed model outperforms existing models on abstract and causal reasoning tasks and is more robust to fine-tuning. |