Addressing Hallucinations in Generative Language Models

深度学习的进步使产生式语言模型(GLMS)成为客户服务以及其他商业应用的潜在游戏规则改变者。与当前的对话式人工智能(CAI)系统不同，这些模型可以在没有明确指令或人工数据的情况下产生自然交互。但是，这种技术带有不正确或捏造答案的风险，即可能会破坏客户信任并损害公司声誉。在这项可行性研究中，我们将建立一个联盟，研究和评估哪些技术可以用来适当地解决GLMS产生的幻觉。这个项目非常适合ALGOMO，因为它改进了我们的核心产品--一个CAI平台，并满足了市场对值得信赖和可靠的生成性CAI的需求。通过开发一种有效地解决幻觉的技术，我们将增加对GLMS产品的信任和信心，最终加速它们的商业采用，不仅是为了客户服务，也是为了各种其他用例。

Advances in Deep Learning have made Generative Language Models (GLMs) a potential game-changer for customer service, as well as other commercial applications. These models can generate natural interactions without explicit instructions or manual data, unlike current conversational Artificial Intelligence (cAI) systems.However, this technology carries the risk of incorrect or fabricated answers, known as "hallucinations" which can undermine customer trust and harm a company's reputation.In this feasibility study, we will build a consortium that will research and assess which technologies could be utilized to properly address hallucinations generated by GLMs.This project fits Algomo perfectly, as it improves upon our core product, a cAI platform, and addresses the market need for trustworthy and reliable generative cAI.By developing a technology that effectively addresses hallucinations, we will increase trust and confidence in the outputs of GLMs, ultimately accelerating their commercial adoption not only for customer service but also for a variety of other use-cases.