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Sullam Jeoung

7 accepted papers

2025

A Systematic Survey of Automatic Prompt Optimization Techniques

EMNLP 2025

Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated bes

Cited by 0SourcePDFScholar
2025

Examining Alignment of Large Language Models through Representative Heuristics: the case of political stereotypes

ICLR 2025poster

Examining the alignment of large language models (LLMs) has become increasingly important, e.g., when LLMs fail to operate as intended. This study examines the alignment of LLMs with human values for the domain of politics. Prior research has shown that LLM-generated outputs can include political l…

2025

Semantic Networks Extracted from Students’ Think-Aloud Data are Correlated with Students’ Learning Performance

EMNLP 2025

When students reflect on their learning from a textbook via think-aloud processes, network representations can be used to capture the concepts and relations from these data. What can we learn from the resulting network representations about students’ learning processes, knowledge acquisition, and le

Cited by 0SourcePDFScholar
2025

Tales of Morality: Comparing Human- and LLM-Generated Moral Stories from Visual Cues

EMNLP 2025

Do moral values align between images, the stories humans write about them, and the narratives generated by large language models (LLMs)? This question matters because stories are central to how humans communicate moral values, yet little is known about how people and LLMs perform this task in a mult

Cited by 0SourcePDFScholar
2023

Detection and Mitigation of the Negative Impact of Dataset Extractivity on Abstractive Summarization

ACL 2023findings

In text summarization, extractivity is defined as a measurement of the degree of overlap between a source document and its summary. Previous research has shown that the extractivity level of training data can influence both output extractivity and the amount of factual information (i.e. faithfulness…

2023

StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models

EMNLP 2023long main

Large Language Models (LLMs) have been observed to encode and perpetuate harmful associations present in the training data. We propose a theoretically grounded framework called StereoMap to gain insights into their perceptions of how demographic groups have been viewed by society. The framework is g…

Cited by 0SourcecodeScholar
2023

Unlearning Bias in Language Models by Partitioning Gradients

ACL 2023findings

Recent research has shown that large-scale pretrained language models, specifically transformers, tend to exhibit issues relating to racism, sexism, religion bias, and toxicity in general. Unfortunately, these pretrained language models are used almost universally in downstream tasks, and natural la…