Papers by Kristen Johnson
Discourse Heuristics For Paradoxically Moral Self-Correction (2025.findings-emnlp)
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| 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. |
ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness (2024.lrec-main)
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| Challenge: | Recent studies in Natural Language Processing (NLP) have unveiled a concerning issue: stereotypical biases associated with demographic groups are prevalent. |
| Approach: | They propose an approach that actively encodes stereotypical biases into the embedding space by integrating stereotypes into a model that acquires agency and belief scores rather than directly representing stereotypes. |
| Outcome: | The proposed model can learn agency and belief stereotypes while preserving the language model’s proficiency. |
Intrinsic Self-correction for Enhanced Morality: An Analysis of Internal Mechanisms and the Superficial Hypothesis (2024.emnlp-main)
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| 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)
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| 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 . |
Classification of Moral Foundations in Microblog Political Discourse (P18-1)
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| Challenge: | a recent study shows correlation between political ideologies and moral foundations expressed in text . a moral foundation theory suggests that there are five basic moral values which underlie human moral perspectives . |
| Approach: | They propose to model the moral foundations of tweets by using an annotation framework . they propose to use policy frames to predict the morality of political tweets . |
| Outcome: | The proposed model can predict moral foundations of political tweets, the authors show . their model can be used to predict political slogans and political ideologies, they say . |
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness (2024.findings-acl)
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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. |
Race, Gender, and Age Biases in Biomedical Masked Language Models (2023.findings-acl)
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| Challenge: | Pre-trained language models can be used to identify and eliminate healthcare disparities. |
| Approach: | They examine social biases present in biomedical masked language models . they curate prompts based on evidence-based practice and compare generated diagnoses . |
| Outcome: | The proposed models are less biased than BERT in gender, while the opposite is true for race and age. |
A Survey to Recent Progress Towards Understanding In-Context Learning (2025.findings-naacl)
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| 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)
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| 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. |