Summary of Gecobench: a Gender-controlled Text Dataset and Benchmark For Quantifying Biases in Explanations, by Rick Wilming et al.
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
by Rick Wilming, Artur Dox, Hjalmar Schulz, Marta Oliveira, Benedict Clark, Stefan Haufe
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 Large pre-trained language models have revolutionized many applications in natural language processing (NLP), serving as the foundation for various downstream tasks. To ensure the quality and transparency of these models, it’s essential to apply ‘explainable artificial intelligence’ (XAI) techniques to their outputs. However, concerns arise about how biases present in training data might affect model weights and behavior, potentially influencing explanations as well. This paper addresses this issue by introducing a gender-controlled text dataset (GECO), which allows for ground-truth ‘world explanations’ for gender classification tasks. The authors also provide GECOBench, a rigorous evaluation framework benchmarking popular XAI methods on pre-trained language models fine-tuned to varying degrees. The study investigates how pre-training influences undesirable bias in model explanations and whether fine-tuning can mitigate such bias. The results show that explanation performance correlates with the number of fine-tuned layers, emphasizing the importance of fine-tuning or retraining embedding layers for XAI methods. This research highlights the value of GECO and GECOBench for developing novel XAI techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart at many tasks! They’re like superheroes in natural language processing (NLP). To make sure they’re good and fair, we need to understand what’s going on inside their heads. One way to do this is by using special AI techniques called ‘explainable artificial intelligence’ (XAI). But here’s the thing: these models are trained on tons of data that might have biases, like gender biases. That could affect how they explain things too! This paper makes a special dataset called GECO that helps us understand how these biases work. It also has a special test to see how well XAI methods do at explaining things. The results show that if we fine-tune the models just right, they can be super good at explaining themselves in a fair way! |
Keywords
» Artificial intelligence » Classification » Embedding » Fine tuning » Natural language processing » Nlp
