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Applicability of Span Prediction
A developer is building a system to extract information from product reviews. One task is to extract the 'product name' mentioned in the review. A second task is to extract all 'mentioned features' of the product, which might be scattered throughout the review text. Explain why a standard span prediction model, which identifies a single start and end position in the text, would be suitable for the first task but would fundamentally fail at the second.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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A language model is tasked with answering a question by identifying the correct text span from a given context. The model works by calculating a probability for each token being the 'start' of the answer and a separate probability for each token being the 'end' of the answer. Consider the following scenario:
Context: 'The first modern Olympic Games were held in Athens, Greece, in 1896. The International Olympic Committee (IOC) was founded in 1894 by Pierre de Coubertin.' Question: 'When was the IOC established?'
The model produces the following highest probabilities:
- Highest Probability Start Token: '1896' (Probability: 0.85)
- Highest Probability End Token: '1894' (Probability: 0.91)
Based on this output, what is the most fundamental reason the model failed to produce a valid answer span?
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Applicability of Span Prediction
BERT-based Architecture for Span Prediction