Editing Nlp (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ NLP Approach Trade-offs
! Approach !! Strengths !! Weaknesses
|-
| Rule-based || Deterministic, interpretable, no data needed || Brittle, doesn't generalize, high maintenance
|-
| Statistical (n-gram) || Simple, fast, works on small data || Can't capture long-range dependencies
|-
| RNN/LSTM || Handles sequences, captures context || Slow to train, vanishing gradients over long sequences
|-
| Transformer (BERT-style) || Rich contextual representations, transfer learning || High compute cost, large memory, positional encoding limits
|-
| LLMs (GPT-4, Claude) || Few-shot learning, generalist capability || Very expensive, hallucination, opaque reasoning
|}

'''Key failure modes:'''
* '''Hallucination''' — LLMs confidently generate factually incorrect text because they learn statistical patterns, not truth.
* '''Bias amplification''' — Models trained on internet text inherit and sometimes amplify demographic biases present in the data.
* '''Out-of-vocabulary (OOV) issue''' — Rare words, misspellings, or domain-specific terminology not in the training vocabulary are poorly handled.
* '''Evaluation mismatch''' — Automated metrics like BLEU and ROUGE correlate poorly with human judgment for generation quality.
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