Papers by Guangliang Liu
Discourse Heuristics For Paradoxically Moral Self-Correction (2025.findings-emnlp)
Copied to clipboard
| Challenge: | moral self-correction is a promising approach for aligning output of Large Language Models with human moral values . authors show that moral self correction relies on discourse constructions that reflect heuristic shortcuts . |
| Approach: | a new method is proposed to strengthen moral self-correction using heuristics extracted from curated datasets. |
| Outcome: | a new method to strengthen moral self-correction is proposed . the proposed method is based on heuristics extracted from curated datasets. |
Intrinsic Self-correction for Enhanced Morality: An Analysis of Internal Mechanisms and the Superficial Hypothesis (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on the effectiveness of moral self-correction in large language models have not been conducted. |
| Approach: | They propose that moral self-correction is a computationally efficient method for reducing harmful content in LLMs. |
| Outcome: | The proposed method reduces harmful content in LLMs, but it remains under-explored . it can help LLM find shortcut to more morally correct output, the authors argue . |
Diagnosing Moral Reasoning Acquisition in Language Models: Pragmatics and Generalization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Prior research has shown that LLMs fail to perform satisfactorily on moral cognizance tasks . |
| Approach: | They propose to use curated datasets to improve LLMs' moral cognizance . they find pragmatic dilemma constrains generalization ability of current learning paradigms . |
| Outcome: | The proposed learning paradigms fail to perform on moral cognizance tasks, the authors show . they show that the pragmatic dilemma is the primary bottleneck for moral reasoning acquisition . |
Dynamic Augmentation Data Selection for Few-shot Text Classification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Data augmentation is a popular method for fine-tuning pre-trained language models to increase model robustness and performance. |
| Approach: | They propose a dynamic data selection method to select effective augmentation data from different augmentation sources according to the model’s learning stage by identifying a set of augmentation samples that optimally facilitates the learning process of the most current model. |
| Outcome: | The proposed method outperforms strong baselines on a variety of sentence classification tasks. |
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness (2024.findings-acl)
Copied to clipboard
Guangliang Liu, Milad Afshari, Xitong Zhang, Zhiyu Xue, Avrajit Ghosh, Bidhan Bashyal, Rongrong Wang, Kristen Johnson
| Challenge: | Debiasing Pretrained Language Models (PLMs) are task-agnostic and can be generalizable, but its impact on language modeling ability and the risk of relearning social biases remain as the two most significant challenges. |
| Approach: | They propose a framework which can Propagate Socially-fair Debiasing to Downstream Fine-tuning to alleviate the forgetting issue of PLMs by regularizing debiased attention heads based on the PLM’s bias levels from stages of pretraining and debiase. |
| Outcome: | The proposed framework can Propagate Socially-fair Debiasing to Downstream Fine-tuning, indicating that the ineffectiveness of debiase can be alleviated by overcoming the forgetting issue through regularizing successfully debiased attention heads based on the PLMs’ bias levels from stages of pretraining and debiases. |
A Survey to Recent Progress Towards Understanding In-Context Learning (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing research on In-Context Learning (ICL) is unclear, despite empirical success . a data generation perspective is used to interpret ICL . |
| Approach: | They propose to use data generation to reinterpret recent efforts from a systematic angle to demonstrate the potential broader usage of ICL. |
| Outcome: | The proposed model can learn from examples provided in the prompt, enabling downstream generalization without the need for gradient updates. |
PAC-tuning: Fine-tuning Pre-trained Language Models with PAC-driven Perturbed Gradient Descent (2023.emnlp-main)
Copied to clipboard
| Challenge: | PAC-tuning is a two-stage fine-tune method for pretrained language models . PAC training minimizes the PACBayes generalization bound to learn proper parameter distribution . |
| Approach: | They propose a two-stage fine-tuning method to minimize the PAC-Bayes generalization bound . they use PAC to inject noise with variance learned in the first stage into the model parameters . |
| Outcome: | The proposed method outperforms baseline methods on 5 GLUE benchmark tasks. |