Papers by Toan Nguyen

4 papers
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models (2024.findings-naacl)

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Challenge: Existing open-source LLMs exhibit limited effectiveness in processing Vietnamese . lack of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation exacerbates these issues.
Approach: They propose to fine tune LLMs specifically for Vietnamese and develop a framework for evaluation . they find that larger models introduce more biases and uncalibrated outputs .
Outcome: The proposed framework finetunes LLMs specifically for Vietnamese and provides a framework for evaluation .
Improving Lexical Choice in Neural Machine Translation (N18-1)

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Challenge: False positives: the output layer rewards frequent words disproportionately, we argue . Falsibles: a model that learns word representations in continuous space tends to translate rare words .
Approach: They propose to fix the norms of both vectors to a constant value and integrate a lexical module which is jointly trained with the rest of the model.
Outcome: The proposed approach achieves improvements of up to +4.3 BLEU surpassing phrase-based translation in nearly all settings.
Causal Direct Preference Optimization for Language Model Alignment (2026.findings-eacl)

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Challenge: Empirical evaluations show that CDPO surpasses DPO-based baselines by achieving unbiased fine-tuning through causal reasoning.
Approach: They propose a framework that incorporates causal inference principles to mitigate the influence of confounders and sharpen the signal of genuine human preferences.
Outcome: The proposed framework preserves the tractability of direct optimization while enhancing robustness to spurious correlations and annotation biases.
Causal Activation Steering via Sparse Mediation (2026.findings-eacl)

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Challenge: a sparse mediation steering approach to control language-model behavior is feasible, says a new study . existing methods that learn dense steering vectors modify thousands of activation dimensions simultaneously .
Approach: They propose a sparse mediation steering approach that learns targeted behavioral interventions via regularized training.
Outcome: The proposed method achieves 97-100% of dense baseline effectiveness across four tasks while using only 10-30% of activation dimensions.

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