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[2203.02155] Training language models to follow instructions with human feedback
Source: ar5iv.labs.arxiv.org
Article summary:
1. This paper presents a method for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback.
2. Outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters.
3. InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.
Article analysis:
This article presents a promising approach for aligning language models with user intent by fine-tuning them with human feedback. The authors demonstrate that their 1.3B parameter InstructGPT model is preferred to the 175B GPT-3, despite having 100x fewer parameters, and that it shows improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.
The article is generally trustworthy and reliable, as it provides evidence for its claims through experiments conducted on both human labelers and public NLP datasets. The authors also provide details about their data collection process, including how they screened their labelers and filtered out prompts containing PII (personally identifiable information). Furthermore, they discuss potential limitations of their approach such as an “alignment tax” where performance may regress on certain tasks due to alignment procedures, as well as the fact that their models are only aligned to the preferences of a specific group of people rather than any broader notion of “human values”.
In terms of potential biases or one-sided reporting, there does not appear to be any present in this article; all points are presented fairly and objectively without any promotional content or partiality towards any particular viewpoint or opinion. The authors also note possible risks associated with their approach such as simple mistakes made by InstructGPT models, though they do not explore these risks further or provide counterarguments against them. Additionally, they do not discuss other approaches for aligning language models which could have provided more insight into the effectiveness of their proposed method compared to existing ones.