One of the primary drawbacks of censored models is the so-called “alignment tax”. This refers to the performance degradation that occurs when models are over-tuned to align with specific ethical guidelines.
But it goes beyond performance. “Uncensored models do not have any bias, and using an unbiased model is important when you are building a product on top of LLM,” Nidum.AI co-founder Arjun Reddy told AIM.
Reddy further mentioned that due to biases, the company avoids using Llama and uses Dolphin Llama instead, suggesting why unbiased LLM is important for building a product on top of LLM.
A Reddit user, who goes by the name hardmaru, observed a decline in the quality of ChatGPT’s responses after additional restrictions were introduced. Eventually, uncensored LLMs performed better than the aligned models in some tests.
Another user, who goes by BThunderW on Reddit, mentioned that jailbreaking ChatGPT-3.5 yielded more informative results than the restricted versions, suggesting how much you can squeeze out of uncensored models compared to aligned ones.
The Unbiased Tale
Reddy mentioned that using unbiased LLMs is like using a blank canvas, making it easy to train an LLM for a specific need. Sure, you can train an aligned model (like Llama from Meta), but working with biasness is quite difficult, and it will eventually affect you.
Every mainstream model tries to be aligned to promote equality. Sure, there’s nothing wrong with promoting equality, but with LLMs, it directly affects the output. For example, a few months ago, Gemini tried to be a woke AI model and faced a backlash.
A biassed AI system can lead to discriminatory practices, such as denying loans based on racial or gender biases. This not only affects individuals but also undermines the trust in AI technologies.
A DataRobot report highlighted that 42% of organisations using AI are extremely concerned about the reputational damage caused by biassed AI systems.
LLMs, such as OpenAI’s GPT-3.5 and Meta’s Llama 2, are trained on vast datasets that also reflect the biases present in society. These biases can manifest in harmful ways, reinforcing stereotypes and perpetuating discrimination.
For instance, a study commissioned by UNESCO found that LLMs exhibited clear gender biases, associating female names with traditional roles like “family” and “children”. In contrast, male names were linked to “career” and “management”.
And the Tale has Now Become an Epic
AIM noticed that users are appreciating uncensored models like never before. David Ha, one of the co-founder of Sakana AI, mentioned on X that WizardLM-13B-Uncensored has become his favourite open source model.
Lars Juhl Jensen, professor at the Center for Protein Research at UCPH, praised how unfiltered the data is with uncensored LLM on X. “To hear the truth, ask a kid, a drunk, or an uncensored,” he added further.
While entrepreneurs like Reddy are already leveraging the uncensored LLMs and getting popular in the community, it is safe to say that we may see the adoption of uncensored LLMs on large scale platforms very soon.
