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🟢 Introduction

This chapter covers how to make completions more reliable, as well as how to implement checks to ensure that outputs are reliable.

To a certain extent, most of the previous techniques covered have to do with improving completion accuracy, and thus reliability, in particular self-consistency1. However, there are a number of other techniques that can be used to improve reliability, beyond basic prompting strategies.

LLMs exhibit various problems including hallucinations2, flawed explanations with CoT methods2, and multiple biases including majority label bias, recency bias, and common token bias3. Additionally, zero-shot CoT can be particularly biased when dealing with sensitive topics4.

Common solutions to some of these problems include calibrators to remove a priori biases, and verifiers to score completions, as well as promoting diversity in completions.

  1. Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022). Self-Consistency Improves Chain of Thought Reasoning in Language Models. ↩
  2. Ye, X., & Durrett, G. (2022). The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning. ↩
  3. Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models. ↩
  4. Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2022). On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning. ↩