14 citations · 15 across the 9 of their papers we have counts for
9 papers
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL
Harper Hua, Zhen Han, Zhengyuan Shen +9
While large language models (LLMs) have substantially improved Text-to-SQL generation, a pronounced gap remains between AI systems and human experts on challenging benchmarks such…
BoundRL: Efficient Structured Text Segmentation through Reinforced Boundary Generation
Haoyuan Li, Zhengyuan Shen, Sullam Jeoung +6
Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts increasingly require segmentation i…
PromptPrism: A Linguistically-Inspired Taxonomy for Prompts
Sullam Jeoung, Yueyan Chen, Yi Zhang +3
Prompts are the interface for eliciting the capabilities of large language models (LLMs). Understanding their structure and components is critical for analyzing LLM behavior and op…
A Systematic Survey of Automatic Prompt Optimization Techniques
Kiran Ramnath, Kang Zhou, Sheng Guan +18
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. Ho…
Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes
Sullam Jeoung, Yubin Ge, Haohan Wang +1
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 wi…
Adaptive Video Understanding Agent: Enhancing efficiency with dynamic frame sampling and feedback-driven reasoning
Sullam Jeoung, Goeric Huybrechts, Bhavana Ganesh +2
Understanding long-form video content presents significant challenges due to its temporal complexity and the substantial computational resources required. In this work, we propose…
Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions
Zhiwen You, HaeJin Lee, Shubhanshu Mishra +4
Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can…
StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models
Sullam Jeoung, Yubin Ge, Jana Diesner
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 Ste…
Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning
Sullam Jeoung, Jana Diesner, Halil Kilicoglu
As language models continue to be integrated into applications of personal and societal relevance, ensuring these models' trustworthiness is crucial, particularly with respect to p…