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Did AI get more negative recently?
Dominik Beese1, Begüm Altunbaş2, Görkem Güzeler2
1Technische Universität Darmstadt, Darmstadt, Hessen, Germany.
Royal Society Open Science
|March 13, 2023
Summary
Scientific articles in natural language processing (NLP) and machine learning (ML) are classified by stance. While papers are increasingly positive, negative stance papers are more cited and growing in number.
Area of Science:
- Artificial Intelligence (AI)
- Natural Language Processing (NLP)
- Machine Learning (ML)
Background:
- Classifying scientific contributions is crucial for understanding research trends.
- Distinguishing between advancing state-of-the-art and critiquing existing work is key.
Purpose of the Study:
- To develop a model for automatically classifying NLP and ML papers by their stance (positive or negative) towards prior work.
- To analyze large-scale trends in the stance of AI research over the past 35 years.
Main Methods:
- Annotated over 1,500 NLP and ML papers to train a SciBERT-based stance detection model.
- Analyzed over 41,000 papers from the last 35 years to identify temporal trends in research stances.
Main Results:
- The proportion of papers with a positive stance has increased over time.
- Negative stance papers have become more critical and are increasing in number.
- Negative stance papers tend to be more influential, receiving more citations.
Conclusions:
- Research in NLP and ML is evolving, with a growing trend towards positive contributions.
- Critical analysis (negative stance) remains important and influential in AI research.
- The study provides insights into the dynamics and impact of different research approaches in AI.
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