The Usefulness of Chatbots

Fools readily declare themselves by opening their mouths

Chatbots are attracting enormous attention, thanks to advances in Natural Language Processing and Deep Learning. The limitations and caveats, however, are still substantial, and the experience of interacting with a bot outside a narrow context can be underwhelming.

Chatbots can use a repository of predefined responses, and can attempt to choose an appropriate response based upon analysis of input and context. This pattern matching retrieval method is limited to the contents of its corpus of interactions. 

Generative conversational models, however, are capable of creating original content organically. These are based upon technology developed for machine translation, in order to better cope with situations where the meaning of a colloquial phrase may be figurative instead of literal. Here, however, the translation is between an input and an output, rather than between languages. Such systems are generally more able to understand the semantic similarities between questions such as ‘what age are you?’ and ‘how old are you?’

Generative techniques were much more challenging and, at the time of writing, not yet in general commercial deployment, though they were where most of the research was going. They require vast amounts of training data. 

Retrieval methods are easier to develop, and though less sophisticated in the range of potential responses, they are less likely to make grammatical errors. However, retrieval methods cannot follow the context of a conversation easily, or refer back to previously mentioned information, or recall physical and demographic context of the conversation partner.

Chatbots are also challenged differently by the length and scope of a conversation. Generally, the longer a conversation continues, the more likely it is to break down. Longer conversations also require more info to be remembered from earlier, or even from previous sessions. Customer support typically involves long, branching conversations, whereas fast commercial interactions may require only one or two messages.

The scope or domain of a conversation may be limited to a particular task or topic, or may be broader or open, where there is no particular goal or intention. Taking a conversation in potentially any direction (open domain) is naturally very challenging. Generally, commercial chatbots are focussed exclusively on providing assistive interactions in a narrowly defined area.

Personality is another aspect, whereby conversational choices may be weighted according to virtual personality vectors, such as agreeableness or openness to experience. This is still a very experimental area, and there is not much sophistication in these processes at present, particularly as training data is normally compiled from a wide range of human personalities.

Replika takes a somewhat different approach, attempting to collate a variety of information from a single user in order to generate a virtual personality based upon conversation style and typical vocabulary. It is also possible to compile a virtual personality from information created by a person who is now deceased, creating a digital echo of them that one may yet converse with. The balance of benefits and harms in such uses of the technology is open to interpretation and remains controversial. On one hand, having a virtual avatar of a loved one could assist with the grieving process as one learns to let go. On the other, it could lead to complications or obsessions for some people.

Chatbots are useful tools when applied in a short conversation of limited scope. By 2026, large language models can sustain broad, multi-turn conversations without amounting to artificial general intelligence, though reliability, memory and safety limits remain. Further progress in areas such as Generative Conversational Models may narrow those remaining gaps.