Papers by Guang-Jie Ren
Evaluating Large Language Models with Enterprise Benchmarks (2025.naacl-industry)
Copied to clipboard
Bing Zhang, Mikio Takeuchi, Ryo Kawahara, Shubhi Asthana, Maruf Hossain, Guang-Jie Ren, Kate Soule, Yifan Mai, Yada Zhu
| Challenge: | Existing benchmarks lack domain-specific datasets for evaluating large language models . existing benchmarks often lack domain specific datasets, which can be difficult to convert to standardized metrics or regulatory issues. |
| Approach: | They propose to use 25 publicly available domain-specific English benchmarks from diverse domains . they propose to combine a wide range of natural language processing tasks for holistic evaluation . |
| Outcome: | The proposed framework includes 25 publicly available domain-specific English benchmarks from diverse enterprise domains like financial services, legal, climate, cyber security, and 2 public Japanese finance benchmarks. |
Don’t be my Doctor! Recognizing Healthcare Advice in Large Language Models (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Large language models (LLMs) are becoming increasingly popular in everyday use, especially in highly regulated domains such as healthcare, where misleading advice may influence users to commit malpractice. |
| Approach: | They present a large-scale health-advice benchmark dataset that evaluates large language models' ability to recognize health-related advice in industrial settings. |
| Outcome: | The proposed model can be misinterpreted as direct advice in highly regulated domains such as healthcare, but the results are not enough to protect them from misinterpreting them as medical advice. |
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)
Copied to clipboard
Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
| Challenge: | Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language. |
| Approach: | They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages. |
| Outcome: | The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research. |
Challenges and Remedies of Domain-Specific Classifiers as LLM Guardrails: Self-Harm as a Case Study (2025.naacl-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have impressive capabilities in generating human-like text, but they pose significant risks in many domains and require guardrails throughout the lifecycle. |
| Approach: | They propose to use a self-harm detector to test the performance of LLM guardrails in real-world environments. |
| Outcome: | The proposed model performs poorly in open and closed domains and is almost unusable in the real world. |