IC-1375GPT-4's constraint satisfaction improves by approximately 20% after one round of automated feedback but plateaus at 66% even after three additional rounds
Shunyu Yao, Howard Chen, Austin W. Hanjie, Runzhe Yang, Karthik R Narasimhan
The paper engages GPT-4 in a generation-feedback dialogue where automated natural language feedback (e.g., 'you generated a word with 3 character v and 4 character i') is provided after each round. GPT-4 shows a significant 20% improvement after the second round, but performance plateaus at 66% after three further rounds, comparable to pass@5 with i.i.d. sampling. Improvement varies by task: word03's rate changes from 62.1% to 10%, while word02, sent01, and sent02 remain challenging throughout.
Evidence
correlational
Key metric
20% improvement after second round of feedback; plateaus at 66% after three additional rounds; comparable to pass@5 using i.i.d. sampling
Caveat
Only GPT-4 was tested in the feedback loop; the paper does not test whether other models show similar or different feedback dynamics.