What’s next in large language model (LLM) research? Here’s what’s coming down the ML pike
There is a lot of excitement around the potential applications of large language models (LLM). We’re already seeing LLMs used in several applications, including composing emails and generating software code.
But as interest in LLMs grows, so do concerns about their limits; this can make it difficult to use them in different applications. Some of these include hallucinating false facts, failing at tasks that require commonsense and consuming large amounts of energy.
Here are some of the research areas that can help address these problems and make LLMs available to more domains in the future.
One of the key problems with LLMs such as ChatGPT and GPT-3 is their tendency to “hallucinate.” These models are trained to generate text that is plausible, not grounded in real facts. This is why they can make up stuff that never happened. Since the release of ChatGPT, many users have pointed out how the model can be prodded into generating text that sounds convincing but is factually incorrect.
One method that can help address this problem is a class of techniques known as “knowledge retrieval.” The basic idea behind knowledge retrieval is to provide the LLM with extra context from an external knowledge source such as Wikipedia or a domain-specific knowledge base.
Google introduced “retrieval-augmented language model pre-training” (REALM) in 2020. When a user provides a prompt to the model, a “neural retriever” module uses the prompt to retrieve relevant documents from a knowledge corpus. The documents and the original prompt are then passed to the LLM, which generates the final output within the context of the knowledge documents.
Work on knowledge retrieval continues to make progress. Recently, AI21 Labs presented “in-context retrieval augmented language modeling,” a technique that makes it easy to implement knowledge retrieval in different black-box and open-source LLMs.
You can also see knowledge retrieval at work in You.com and the version of ChatGPT used in Bing. After receiving the prompt, the LLM first creates a search query, then retrieves documents and generates its output using those sources. It also provides links to the sources, which is very useful for verifying the information that the model produces. Knowledge retrieval is not a perfect solution and still makes mistakes. But it seems to be one step in the right direction.
Despite their impressive results, LLMs do not understand language and the world — at least not in the way that humans do. Therefore, there will always be instances where they will behave unexpectedly and make mistakes that seem dumb to humans.
One way to address this challenge is “prompt engineering,” a set of techniques for crafting prompts that guide LLMs to produce more reliable output. Some prompt engineering methods involve creating “few-shot learning” examples, where you prepend your prompt with a few similar examples and the desired output. The model uses these examples as guides when producing its output. By creating datasets of few-shot examples, companies can improve the performance of LLMs without the need to retrain or fine-tune them.
