OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs (2024.naacl-demo)
Copied to clipboard
| Challenge: | Current research seeks to de-bias such models, or suppress potentially biased answers. |
| Approach: | They present a web demo to test the biases of instruction-tuned Large Language Models . they identify 11 different biase based on a corpus of data . |
| Outcome: | The proposed demo shows that biases in instruction-tuning are explicit and transparent . the demo shows how the model was trained and showcases the web application . |
Similar Papers
LLM Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies show that instruction-tuned LLMs under-predict positive classes . however, they are overly sensitive and can be applied for abuse detection without fine-tuning . |
| Approach: | They show that instruction-tuned LLMs tend to under-predict positive classes . they also show that label frequency in the prompt helps with the significant over-prediction . |
| Outcome: | The proposed models under-predict positive classes in social media, whereas they are overly sensitive. |
OffsetBias: Leveraging Debiased Data for Tuning Evaluators (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have identified that judge models are vulnerable to certain biases, such as favoring longer answers regardless of content. |
| Approach: | They propose to use open-source LLMs to fine-tune judge models and propose a meta-evaluation collection of test cases for each bias type. |
| Outcome: | The proposed meta-evaluation dataset shows that fine-tuning improves the robustness of judge models against biases and improves performance across most evaluation scenarios. |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have shown that relying on LLMs as information providers may hurt student learning. |
| Approach: | They introduce and apply two bias score metrics to evaluate LLMs for bias in the personalized educational setting, specifically on the models’ roles as “teachers.” |
| Outcome: | The proposed models harm student learning by perpetuating harmful stereotypes and reversing them. |
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs (2025.naacl-long)
Copied to clipboard
Do Xuan Long, Ngoc-Hai Nguyen, Tiviatis Sim, Hieu Dao, Shafiq Joty, Kenji Kawaguchi, Nancy F. Chen, Min-Yen Kan
| Challenge: | Using format-following capabilities, state-of-the-art large language models (LLMs) can be leveraged to tailor outputs to specific task formats. |
| Approach: | They propose to define a format bias evaluation metric and establish effective strategies to reduce it. |
| Outcome: | The proposed evaluation reduces the variance in ChatGPT’s performance among wrapping formats from 235.33 to 0.71 (%2) |
How Susceptible are Large Language Models to Ideological Manipulation? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have the potential to exert substantial influence on public perceptions and interactions with information. |
| Approach: | They examine how LLMs can learn and generalize ideological biases from their instruction-tuning data. |
| Outcome: | The LLMs show a startling ability to absorb ideology from one topic and generalize it to even unrelated ones. |
Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on LLMs undertaking political questionnaires, which offers only limited insights into their biases and operational nuances. |
| Approach: | They propose to use a curated dataset to generate 56,700 synthetic articles using nine LLMs. |
| Outcome: | The proposed model can detect political biases using supervised models and LLMs. |
LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings. |
| Approach: | They propose to analyze 156k LLM responses to 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. |
| Outcome: | The proposed analysis of 156k LLM responses to the Political Compass Test (PCT) generated by 6 LLMs shows that tropes are recurrent and consistent across prompts. |
Fine-Tuned LLMs are “Time Capsules” for Tracking Societal Bias Through Books (2025.naacl-long)
Copied to clipboard
| Challenge: | We develop a corpus comprising 593 fictional books across seven decades (1950-2019) to track bias evolution. |
| Approach: | They develop a method to trace and quantify bias evolution using fine-tuned LLMs on fictional books across seven decades to track bias evolution. |
| Outcome: | The proposed method traces and quantifies bias evolution in a corpus of 593 fictional books across seven decades. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |