Shifting order in multiple-choice questions massively affects LLM performance

Recent research proposes that Large Language Models (LLMs) may not be as reliable as we think. In fact, the order of options in a multiple-choice question drastically influences the responses from LLMs such as GPT-4 and InstructGPT.

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What are the findings?

• LLM sensitivity to multiple-choice arrangement: The study suggests if options in multiple-choice questions are reordered, the LLM's performance varies dramatically— approximately 13% to 75% depending on the benchmark.
• Positional bias shapes responses: When the LLM is uncertain between top-selected answers, the option positioning can artificially lean its predictions. Observations also found that LLMs favor specific placements when unsure of the optimal response among top-selected answers.
• Performance improves when calibration techniques are applied: Making use of two unique calibration methods, the performance of LLMS saw up to eight percentage points of increase across numerous models and benchmarks.

Why does this matter?

This moves us closer to identifying the factors contributing to LLMs' sensitivity and highlights the significance of recognizing and confronting these sensitivities to improve real-world usability and reliability.

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(arXiv)