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Five jumbled up sentences (labelled 1, 2, 3, 4 and 5), related to a topic, are given below. Four of them can be put together to form a coherent paragraph. Identify the odd sentence and key in the number of that sentence as your answer.

  1. Machine learning models are prone to learning human-like biases from the training data that feeds these algorithms.
  2. Hate speech detection is part of the on-going effort against oppressive and abusive language on social media.
  3. The current automatic detection models miss out on something vital: context.
  4. It uses complex algorithms to flag racist or violent speech faster and better than human beings alone.
  5. For instance, algorithms struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways because they're trained on imbalanced datasets with unusually high rates of hate speech.

Entered answer:

Solution

✅ Correct Answer: 3

Step 1: Find the main topic -> All sentences talk about hate speech detection systems, butwe need to see which four connect smoothly.

Step 2: Look for the flow of ideas -> Sentences should build on each other or connect logically.

From passage: "Machine learning models are prone to learning human-like biases" and "For instance, algorithms struggle to determine if group identifiers like 'gay' or 'black' are used in offensive ways"

The main story here: Hate speech detection exists -> It uses algorithms -> But it has bias problems -> Here's an example of bias.


Thought Process:

Sentence 2 -> introduces hate speech detection

Sentence 4 -> explains how it works (algorithms)

Sentence 1 -> explains the main problem (bias from training data)

Sentence 5 -> gives a specific example of this bias problem

These four connect perfectly: what it is -> how it works -> what's wrong -> example

Sentence 3 -> talks about missing "context" which is a totally different issue than bias


Why Sentence 3 is the odd one out:

🔴 Sentence 3 is incorrect -> scope-error -> introduces "context" as a problem, but the other four sentences focus specifically on bias issues, not context issues. It shifts to a different type of problem that doesn't connect with the bias discussion.

The coherent paragraph flows: hate speech detection systems (2) use complex algorithms (4) but suffer from human-like biases in training data (1), for example with words like "gay" or "black" (5).

Sentence 3 brings up context understanding, which is unrelated to the bias theme running through the other four sentences.

Answer: 3

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