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A Negative Problem for Large Language Models

A Negative Problem for Large Language Models
by Bernard Murphy on 05-23-2023 at 6:00 am

I recently read a thought-provoking article in Quanta titled Chatbots Don’t Know What Stuff Isn’t. The point of the article is that while large language models (LLMs) such as GPT, Bard and their brethren are impressively capable, they stumble on negation. An example offered in the article suggests that while a prompt, “Is it true that a bird can fly?”, would be answered positively with prolific examples, the inverse, “Is it true that a bird cannot fly?”, will likely also produce a positive answer supported by the same examples. The word “not” is effectively invisible to LLMs, at least today.

A Negative Problem for Large Language Models

The Quanta article is well worth reading, as are most Quanta articles. What is especially interesting is that fixing LLMs to manage negatives reliably is proving to be more challenging than at first thought. I see two interesting ways to frame the problem, first a computer science analysis, second in asking what we mean by “not”.

Why do LLMs struggle with negation?

These models learn, from spectacularly large amounts of data, to generate a model of reality. An LLM builds a model of likelihoods of sequences of words associated with corresponding topics. There is no place in such a model to handle negation of a word. How would it be possible for inference to map “not X” as a term when the deep learning model is built on training data in which terms are necessarily positive (“X” rather than “not X”)?

SQL selections routinely handle negative terms – “select all clients who are not in the US” (I’m being casual with syntax). Why couldn’t LLMs do the same thing? They could in training use a similar selection mechanism to pre-determine what data should be used for training. But then the model would be trained explicitly to handle prompts with that specific negation, blocking hope of answering prompts about clients who are in the US. What we really want is a trained model which can answer prompts for both “in the US” and “not in the US”, which seems to require two models. That’s just to cover one negation possibility. As the number of terms which might be negated increases, the number of models (and time to train and infer) grows exponentially.

Research suggests that ChatGPT has improved a little in handling negatives and antonyms through human-in-the-loop training. However, experts claim developers are chipping away at the problem rather than finding major breakthroughs. When you consider the significant range of possibilities in expressing a negative (explicit negation or use of an antonym, both allowing for many ways of re-phrasing), this perhaps should not be too surprising.

What do we mean by “not”?

“Not” in natural language carries a wealth of meaning which is not immediately apparent from a CS viewpoint. We want “not” to imply a simple inverse but consider the earlier example “Is it true that a bird cannot fly?”. Many birds can (robins, ducks, eagles), some cannot (penguins, some species of steamer duck, ostriches), and some can manage a little but not sustained flight (chickens). Some mammals can glide (flying squirrels); are they birds? The question doesn’t admit a simple yes/no answer. An LLM would likely present these options, ignoring the “not” but not really answering the question in a way that would demonstrate understanding. That is good enough for a search but is hardly a foundation for putting us all out of work.

“Not” provides a simple demonstration that meaning cannot be extracted from text by statistical analysis alone, no matter how large the training dataset. At some point meaning must tap into “commonsense”, all the implicit understanding we have in using language. “Not” highlights this dependency because “not X” implies absolutely everything – not including X – is possible. We deal with this crazy option in real life through commonsense, eliminating all except reasonable options. An LLM can’t do that because (as far as I know) there is no corpus for commonsense. LLMs can be patched through human guidance to do better on specific cases, but I am skeptical that patching can generalize.

LLM has demonstrated amazing capabilities, but like any technology we build it has limits which are becoming clearer, thanks in part to one seemingly inoffensive word.

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