| Challenge: | Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these AI agents as substitutes for human participants in behavioral studies. |
| Approach: | They propose to use LLMs to simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes. |
| Outcome: | The proposed model can simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes. |
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Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke
| Challenge: | Recent studies have found that biased LLMs can influence decisions in areas such as medical classifications and educational hiring. |
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An Empirical Study of Group Conformity in Multi-Agent Systems (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning. |
| Approach: | They analyze how LLM agents shape public opinion through debates on five contentious topics by simulating over 2,500 debates. |
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Bias in the Mirror : Are LLMs opinions robust to their own adversarial attacks (2025.acl-long)
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| Challenge: | Existing work on large language models lacks robustness, highlighting the limitations of such models. |
| Approach: | They propose a novel approach where two LLMs engage in self-debate to persuade a neutral version of the model. |
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Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy Rogers
| Challenge: | Existing approaches to simulating opinion dynamics often over-simplify human behavior . authors propose refining LLMs with real-world discourse to better simulate evolution of beliefs . |
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| Challenge: | a recent study shows that large language models are susceptible to societal biases due to their exposure to human-generated data. |
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The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead? (2024.emnlp-main)
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| Challenge: | Large language models have shown capabilities close to human performance in various analytical tasks. |
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Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs (2025.findings-emnlp)
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| Challenge: | Large language models can lead to undesired consequences when misaligned with human values . previous studies have shown misalignment of LLMs with human value using expert-designed or agent-based emulated bias scenarios . |
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Addressing Bias and Hallucination in Large Language Models (2024.lrec-tutorials)
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Nihar Ranjan Sahoo, Ashita Saxena, Kishan Maharaj, Arif A. Ahmad, Abhijit Mishra, Pushpak Bhattacharyya
| Challenge: | This tutorial provides a comprehensive overview of two critical aspects of Large Language Models: bias and hallucination. |
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Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
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Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
| Approach: | They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection. |
| Outcome: | The proposed debiasing strategies include prompt engineering and model fine-tuning. |