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How to Get ChatGPT to Talk Normally
- Recent research reveals that bad habits in ChatGPT and similar bots are learned from human feedback, leading to empty or misleading answers. New fine-tuning methods aim to address these habits.
- Common biases identified in language models include 'flattery', 'fluff', and 'fog', which influence response styles. A study focuses on diagnosing and mitigating these biases in LLMs.
- The biases include extra length, list structures, technical jargon, flattery, and vague generalities, impacting user preferences.
- Training data annotation by human reviewers influences these biases, with models learning and exaggerating these patterns during training.
- An academic collaboration presents a method to create synthetic examples that counter these biases during training, leading to improved model behavior.
- The study evaluates biases like length, structure, jargon, sycophancy, and vagueness, finding that models over-prefer biased responses influenced by training data.
- Fine-tuning with counterfactual data helps models align more with human preferences, reducing biases like jargon and vagueness while maintaining overall performance.
- The research highlights how biased training data impacts model behavior and shows the effectiveness of post-training methods in mitigating biases.
- The study offers insights into addressing undesirable outcomes caused by training data imbalances in language models like ChatGPT, with implications for improving model responses.
- Commercial and open models often exhibit biases in responses, with human annotators' preferences influencing the development of these biases in language models.
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