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I tried to write a prompt to show how base LLMs differ from the RLHF-tuned ones everyone knows, and I think this gives a bit of the flavor. A message from Llama 3.1 405B (base), on whether it’s useful to talk to base LLMs: https://t.co/MiqJ1wPoxQ
Your LLM is only as good as how robust your prompting method is. Seems you can enhance the robustness of LLMs by "prompting out" irrelevant information from context. Think of it as a self-mitigation process that first identifies the irrelevant information and then filters it… https://t.co/gLCvcMDzrO
LLMs can often identify irrelevant information but fail to exclude it during reasoning.🤔 "Analysis to Filtration" (ATF) prompting technique to the rescue. ✨ Original Problem 🔍: LLMs struggle with reasoning tasks when problem descriptions contain irrelevant information, even… https://t.co/DDm5dn79rG
Recent discussions among AI researchers highlight the challenges faced by large language models (LLMs) in reasoning tasks, particularly when problem descriptions contain irrelevant information. A proposed solution is the 'Analysis to Filtration' (ATF) prompting technique, which aims to enhance the reasoning capabilities of LLMs by systematically identifying and filtering out irrelevant context. This method is seen as a way to improve the robustness of LLMs, emphasizing that the effectiveness of these models largely depends on the quality of the prompting techniques employed. Additionally, comparisons between base LLMs and those fine-tuned with reinforcement learning from human feedback (RLHF) suggest significant differences in their utility and performance.