Post by account_disabled on Mar 11, 2024 8:45:43 GMT
Language models such as GPT-3 are extremely powerful today and are used to produce content in various fields. However, training derived from online material can lead to the production of toxic information and misinformation. What is InstructGPT? Let's see what it's all about! Alessio Pomaro Alessio Pomaro Feb 15, 2022 •7 min read GPT-3 and the generation of "toxic" texts: the new InstructGPT models GPT-3 and the generation of "toxic" texts: the new InstructGPT models Large language models ( LLMs ) such as OpenAI 's GPT-3 can now " write " texts that, under certain conditions, are indistinguishable from content composed by humans.
In the last experiment, I had some people (including copywriters) read a India Mobile Number Data text created through GPT-3 by asking: "what rating would you give to this copy?". No one had the slightest suspicion about the nature of the contents. The areas of use of LLM models are wide, ranging from the creation of marketing content to video games, recipes, poems and film screenplays. But as these systems are educated and " trained " using existing online content, they can learn " toxic " information and produce texts that cause misinformation , and affect the sensitivity of some people ( e.g. through expressions of sexism, racism, etc. ). Over time, several efforts have been made to combat toxicity in LLM models , with not always optimal results.
The following posts describe some examples. Researchers proposed bias fix for GPT-3 and other language models A study shows that the accuracy of language models like GPT-3 can be improved through careful calibration. VentureBeat Kyle Wiggers Double Hard-Debias: Tailoring Word Embeddings for Gender Bias Mitigation Word embeddings inherit strong gender bias in data which can be further amplified by downstream models. We propose to purify word embeddings against corpus regularities such as word frequency prior to inferring and removing the gender subspace, which significantly improves the debiasing performance. SalesforceResearch Tianlu Wang OpenAI's InstructGPT models OpenAI recently said it has developed a new family of templates ( InstructGPT ) that are less likely to generate problematic text, and that align more closely with user intent .
In the last experiment, I had some people (including copywriters) read a India Mobile Number Data text created through GPT-3 by asking: "what rating would you give to this copy?". No one had the slightest suspicion about the nature of the contents. The areas of use of LLM models are wide, ranging from the creation of marketing content to video games, recipes, poems and film screenplays. But as these systems are educated and " trained " using existing online content, they can learn " toxic " information and produce texts that cause misinformation , and affect the sensitivity of some people ( e.g. through expressions of sexism, racism, etc. ). Over time, several efforts have been made to combat toxicity in LLM models , with not always optimal results.
The following posts describe some examples. Researchers proposed bias fix for GPT-3 and other language models A study shows that the accuracy of language models like GPT-3 can be improved through careful calibration. VentureBeat Kyle Wiggers Double Hard-Debias: Tailoring Word Embeddings for Gender Bias Mitigation Word embeddings inherit strong gender bias in data which can be further amplified by downstream models. We propose to purify word embeddings against corpus regularities such as word frequency prior to inferring and removing the gender subspace, which significantly improves the debiasing performance. SalesforceResearch Tianlu Wang OpenAI's InstructGPT models OpenAI recently said it has developed a new family of templates ( InstructGPT ) that are less likely to generate problematic text, and that align more closely with user intent .