activity
20222026
most cited"My Answer is C": First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models

3 citations · 10 across the 14 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024★ 1 cited

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-…

cs.CL2024★ 3 cited

"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…

cs.CL2024★ 1 cited

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…

cs.CL2024

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…