zhou-2023-llama2-mr-reduction-mrc-re
IN premise — summaries/2026/08/24/zhou-2023-context-faithful-prompting-s4-experiments.md
Created 2026-08-25T02:59:10+00:00
For LLaMA-2-7B-chat, the OPIN+INSTR+Counterfactual method achieved a 39.4% reduction in MR for MRC and 57.3% reduction for RE compared to zero-shot baseline.
Summary
A specific prompting strategy that combines opinion framing, explicit instruction, and counterfactual reasoning cuts the target error rate by roughly 40 percent on reading comprehension tasks and over 57 percent on relation extraction tasks for the 7-billion-parameter LLaMA-2 chat model. This matters because it shows the model's baseline error rate is not fixed and can be meaningfully reduced through prompt design, which supports any downstream conclusions that assume the model can be steered away from its default failure modes.