BIG-Bench Mistake: Implementational Details That Are Important
#llms #bigbenchmistake #cotprompting #bigbenchdatasets #cotstyletraces #palm2 #3shotprompting #aiprompts
https://hackernoon.com/big-bench-mistake-implementational-details-that-are-important
#llms #bigbenchmistake #cotprompting #bigbenchdatasets #cotstyletraces #palm2 #3shotprompting #aiprompts
https://hackernoon.com/big-bench-mistake-implementational-details-that-are-important
Hackernoon
BIG-Bench Mistake: Implementational Details That Are Important | HackerNoon
We use PaLM 2 L (Unicorn) to generate the traces used in BIG-Bench Mistake. All traces are generated at temperature = 0. We algorithmically append “Thought N:”
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.