Summary of Examining the Influence Of Political Bias on Large Language Model Performance in Stance Classification, by Lynnette Hui Xian Ng et al.
Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification
by Lynnette Hui Xian Ng, Iain Cruickshank, Roy Ka-Wei Lee
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research paper explores the political biases of Large Language Models (LLMs) in performing tasks related to natural language queries. Despite their impressive capabilities, these models are trained on curated datasets that inherently contain biases ranging from racial and national to gender biases. The study investigates whether these biases affect the performance of LLMs for specific tasks, specifically stance classification. Using three datasets, seven LLMs, and four prompting schemes, the authors analyze the models’ performance on politically-oriented statements and targets. The findings reveal statistically significant differences in performance across various stance classification tasks, primarily driven by the dataset level rather than model or prompting scheme variations. Moreover, the study shows that when there is greater ambiguity in the target, LLMs exhibit poorer stance classification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how Large Language Models (LLMs) perform on tasks related to political opinions. Even though these models are very good at what they do, they’re trained using data that can be biased against certain groups of people. The study wants to see if this bias affects the models’ performance on specific tasks, like figuring out whether someone agrees or disagrees with a statement. The researchers used different datasets, models, and ways of asking questions to test how well the models do. They found that the models don’t all perform equally well, but it’s mainly because of where they got their training data from. When it’s harder to tell what someone is agreeing or disagreeing with, the models get worse at figuring it out. |
Keywords
» Artificial intelligence » Classification » Prompting
