Yanchen Liu

selected publications

1. 

   Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classification

   Yanchen Liu, Srishti Gautam, Jiaqi Ma, and Himabindu Lakkaraju

   In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics

   NAACL 2024

   Abs arXiv Bib

   Recent literature has suggested the potential of using large language models (LLMs) to make predictions for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in the society. To this end, as well as the widespread use of tabular data in many high-stake applications, it is imperative to explore the following questions: what sources of information do LLMs draw upon when making predictions for tabular tasks; whether and to what extent are LLM predictions for tabular tasks influenced by social biases and stereotypes; and what are the consequential implications for fairness? Through a series of experiments, we delve into these questions and show that LLMs tend to inherit social biases from their training data which significantly impact their fairness in tabular prediction tasks. Furthermore, our investigations show that in the context of bias mitigation, though in-context learning and fine-tuning have a moderate effect, the fairness metric gap between different subgroups is still larger than that in traditional machine learning models, such as Random Forest and shallow Neural Networks. This observation emphasizes that the social biases are inherent within the LLMs themselves and inherited from their pre-training corpus, not only from the downstream task datasets. Besides, we demonstrate that label-flipping of in-context examples can significantly reduce biases, further highlighting the presence of inherent bias within LLMs.

   @inproceedings{Liu2023TabularBiases,
     title = {Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classification},
     author = {Liu, Yanchen and Gautam, Srishti and Ma, Jiaqi and Lakkaraju, Himabindu},
     year = {2023},
     publisher = {EMNLP 2023},
     booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics},
   }

2. 

   DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

   Yanchen Liu, William Held, and Diyi Yang

   In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

   EMNLP 2023

   Abs arXiv Bib HTML

   Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identification systems. The boundaries between dialects are inherently flexible, making it difficult to categorize language into discrete predefined categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features. The compositional architecture of DADA allows for both targeted adaptation to specific dialect variants and simultaneous adaptation to various dialects. We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.

   @inproceedings{Liu2023DADA,
     title = {DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules},
     author = {Liu, Yanchen and Held, William and Yang, Diyi},
     year = {2023},
     html = {https://liuyanchen1015.github.io/DADA/},
     publisher = {EMNLP 2023},
     booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
   }

3. 

   Semantic-Oriented Unlabeled Priming for Large-Scale Language Models

   Yanchen Liu, Timo Schick, and Hinrich Schütze

   In ACL Workshop on Simple and Efficient Natural Language Processing, 2022

   ACLW 2023

   Abs arXiv Bib

   Due to the high costs associated with finetuning large language models, various recent works propose to adapt them to specific tasks without any parameter updates through in-context learning. Unfortunately, for in-context learning there is currently no way to leverage unlabeled data, which is often much easier to obtain in large quantities than labeled examples. In this work, we therefore investigate ways to make use of unlabeled examples to improve the zero-shot performance of pretrained language models without any finetuning: We introduce Semantic-Oriented Unlabeled Priming (SOUP), a method that classifies examples by retrieving semantically similar unlabeled examples, assigning labels to them in a zero-shot fashion, and then using them for in-context learning. We also propose bag-of-contexts priming, a new priming strategy that is more suitable for our setting and enables the usage of more examples than fit into the context window.

   @inproceedings{Liu2022SemanticPriming,
     title = {Semantic-Oriented Unlabeled Priming for Large-Scale Language Models},
     author = {Liu, Yanchen and Schick, Timo and Schütze, Hinrich},
     year = {2022},
     publisher = {ACL 2023},
     booktitle = {ACL Workshop on Simple and Efficient Natural Language Processing, 2022},
   }