3 citations · 10 across the 14 of their papers we have counts for
14 papers
Moral Lenses, Political Coordinates: Towards Ideological Positioning of Morally Conditioned LLMs
Chenchen Yuan, Bolei Ma, Zheyu Zhang +3
While recent research has systematically documented political orientation in large language models (LLMs), existing evaluations rely primarily on direct probing or demographic pers…
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges
Bolei Ma, Yuting Li, Wei Zhou +7
Understanding pragmatics-the use of language in context-is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language tech…
Aligning NLP Models with Target Population Perspectives using PAIR: Population-Aligned Instance Replication
Stephanie Eckman, Bolei Ma, Christoph Kern +3
Models trained on crowdsourced annotations may not reflect population views, if those who work as annotators do not represent the broader population. In this paper, we propose PAIR…
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study
Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch +5
In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the abi…
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models
Bolei Ma, Xinpeng Wang, Tiancheng Hu +4
Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may capture and convey. T…
Look at the Text: Instruction-Tuned Language Models are More Robust Multiple Choice Selectors than You Think
Xinpeng Wang, Chengzhi Hu, Bolei Ma +2
Multiple choice questions (MCQs) are commonly used to evaluate the capabilities of large language models (LLMs). One common way to evaluate the model response is to rank the candid…
Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
Ercong Nie, Shuzhou Yuan, Bolei Ma +4
Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-…
"My Answer is C": First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models
Xinpeng Wang, Bolei Ma, Chengzhi Hu +5
The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging. One common evaluation approach uses multiple-choice qu…
Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers
Shuzhou Yuan, Ercong Nie, Bolei Ma +1
Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. However, the sheer size of these models poses challenge…
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
Bolei Ma, Ercong Nie, Shuzhou Yuan +4
Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily f…
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
Christoph Kern, Stephanie Eckman, Jacob Beck +3
When training data are collected from human annotators, the design of the annotation instrument, the instructions given to annotators, the characteristics of the annotators, and th…
Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
Zheyu Zhang, Han Yang, Bolei Ma +2
Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability. This…
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding
Bolei Ma, Ercong Nie, Helmut Schmid +1
Multilingual pretrained language models (MPLMs) have demonstrated substantial performance improvements in zero-shot cross-lingual transfer across various natural language understan…
What cleaves? Is proteasomal cleavage prediction reaching a ceiling?
Ingo Ziegler, Bolei Ma, Ercong Nie +4
Epitope vaccines are a promising direction to enable precision treatment for cancer, autoimmune diseases, and allergies. Effectively designing such vaccines requires accurate predi…