What Are the Benchmark Results of GPT-4-Turbo, GPT4, and GPT-3.5-Turbo?
#llms #gptbenchmarkresults #bigbenchmistake #directtracelevelprompting #cotsteplevelprompting #directsteplevelprompting #llmoutputcorrection #usingllmstofindmistakes
https://hackernoon.com/what-are-the-benchmark-results-of-gpt-4-turbo-gpt4-and-gpt-35-turbo
#llms #gptbenchmarkresults #bigbenchmistake #directtracelevelprompting #cotsteplevelprompting #directsteplevelprompting #llmoutputcorrection #usingllmstofindmistakes
https://hackernoon.com/what-are-the-benchmark-results-of-gpt-4-turbo-gpt4-and-gpt-35-turbo
Hackernoon
What Are the Benchmark Results of GPT-4-Turbo, GPT4, and GPT-3.5-Turbo? | HackerNoon
All models are given the same 3-shot prompts. We use three different prompting methods. Direct trace-level prompting involves using the whole trace as input
LLMs Can Correct Reasoning Errors! But Not Without Limitations
#llms #bigbenchmistake #cotstyletraces #usingllmstocorrecterrors #rewardmodels #usingllmstofindmistakes #humanannotation #llmbacktracking
https://hackernoon.com/llms-can-correct-reasoning-errors-but-not-without-limitations
#llms #bigbenchmistake #cotstyletraces #usingllmstocorrecterrors #rewardmodels #usingllmstofindmistakes #humanannotation #llmbacktracking
https://hackernoon.com/llms-can-correct-reasoning-errors-but-not-without-limitations
Hackernoon
LLMs Can Correct Reasoning Errors! But Not Without Limitations | HackerNoon
In this paper, we describe and release our dataset BIG-Bench Mistake for mistake-finding and propose a backtracking method to correct logical errors.