Template:M intro work Large Learning Model: Difference between revisions

{{d|LLM|/ɛl ɛl ɛm/ (''also “[[large language model]]”'')|n|}}

Once upon a time, an [[LLM]] was a “Master of Laws”: the postgraduate mark of the ''sensei'' in the society of legal service providers — either of that, or of the indolence of one not prepared to strike out and put what she has learned into practice — but still: it spoke to perseverance, depth, comprehension and mastery, however pigeon-hearted its motivation.

''Who benefits'', primarily, from this emergent technology? Experience should tell us that the first  — and often the ''last'' — to benefit from legal productivity tools are the ''lawyers''. Should we expect [[this time is different|this time be different]]?

And bear in mind it will be the lawyers who deploy [[LLM]]s as a tool, not their clients. Why? ''Because of that [[ineffability]]''. An [[LLM]] is a pattern-matching device. It understands nothing. It cannot provide unmediated legal advice. It can only ever be a “back-breaker”: the “last mile” needs a human who knows what she is doing, understands the context and complicated human psychology at play in the cauldron of 1 [[negotiation]]. An [[LLM]] can draw pretty, impressive-at-a-distance doodles, but it cannot do that. Nor can it write [[legal opinion]]s — well, not meaningful ones — and nor, unmediated by a law firm, does it have the insurance policy or deep, suable pockets for which a client is paying when it seeks legal advice in the first place.

“But, [[JC]], come on. Be realistic. It is dog-eat-dog out there. Any lawyer keeps the bounty of the [[LLM]] from her clients will soon have her lunch eaten by others who won’t. You cannot fight the invisible hand. We are in a race to the bottom.”

Nor should we underestimate the overwhelming power of the lawyer’s intuition that ''what has gone before is sacrosanct''.

''Writing'' is perhaps the greatest of human inventions, binding together people who never knew each other, citizens of distant epochs. Books break the shackles of time. A book is proof that humans are capable of working magic.”

Fittingly, the first [[chatbot]] was a designed as a parlour trick. In 1966 Joseph Weizenbaum, a computer scientist at MIT created the programme “[[ELIZA]]” to explore communication between humans and machines. [[ELIZA]] used pattern matching and substitution techniques to generate realistic conversations. You can try it out [https://web.njit.edu/~ronkowit/eliza.html here].

Even though [[ELIZA]] was a basic “keepy uppy” machine, it proved surprisingly addictive. Weizenbaum was famously shocked how easily people were prepared to believe [[ELIZA]] “understood” them and contributed meaningfully to the interaction. When trying it out, Weizenbaum’s own secretary, who had watched him build the programme for months and knew how it worked, shooed him out of the room so she could have privacy with the machine.  

''[[LLM]]s work the same way''.  

Like all good conjuring tricks, [[generative AI]] relies on misdirection: its singular genius is that it lets us misdirect ''ourselves''. We wilfully suspend disbelief, never noticing who is creatively re-rendering machine-made screed as magic. ''We are''. We are neuro-linguistically programming ''ourselves'' to be wowed by a {{plainlink|https://en.wikipedia.org/wiki/Digesting_Duck|mechanical duck}}, when the clever part is really happening inside our own heads. Humans are seldom stupider than when they misattributing their own genius to a box of gears and pulleys.

By writing “prompts”, we create an expectation of what we will see. When the pattern-matching machine produces something roughly like that, we use our own imaginations to backfill, frame, filter, correct, boost, render, sharpen and polish the output into what we ''wanted'' to see. We ''construe'' the output to conform to our original instructions.  

When we say, “fetch me a tennis racquet”, and the machine comes back with something more like a lacrosse stick, we are far more impressed than we would be of a human who did the same thing: we would think such a human a bit ''dim''. But with [[generative AI]] we don’t, at first, even notice we are not getting what we asked for. We might think, “oh, nice try!” or “well, that will do,” or perhaps, “ok, computer: try again, but make the basket bigger, the handle shorter, and tighten up the net.” We can iterate this way until we have what we want — though note all the refining intelligence is coming from ''us'' — or we could just Google for a conventional photo of a tennis racquet. As the novelty wears off, that is what more and more people will do.

Either way, as we persevere, we see more and more of the magician’s wires. We sense how the model works: its familiar tropes, tics and consistent ways of doing things, which are never quite what you had in mind. The first couple of passes are great: they get from zero to 50% of what we wanted. But the marginal improvement in each following round diminishes. The piquant surprise at what the machine can produce dampens at each go-round, reaching asymptotically towards an upper capability, well short of 100%, in “producing what you had in mind”. Eventually we settling into an [[Entropy|entropic]] and vaguely dissatisfying quotidian. “Okay, it isn’t quite what I had in mind. but in the interests of time it will do.”   

Again, we subordinate our own interests to the machine’s. We accept a mediocre job we would not take from a human. We must be more exacting lest we let the machines become our overlords, not by conquest but our own pathetic surrender.

Ours is the skill going in, and ours is the skill construing the output. The machine does the boring bit:  the bit that machines have ''always'' done: applies uninspired strength, speed, reliability, and economy. ''Not ingenuity''.

:— ''Hamlet'' I, ''v''.}}

Now, in all kinds of literature ''bar one'', “reading” is where the real magic happens. ''Construal''. It is the [[Emergent|emergent]] creative act and community consensus that renders ''Hamlet'' a timeless cultural leviathan and {{br|Dracula: The Undead}} forgettable pap.<ref>Maybe not ''that'' forgettable, come to think of it: it has stayed with me 15 years, after all.</ref> A literary work may start with the text, but it stays there barely a moment. The “meaning” of a literary work is ''necessarily personal'' to the reader: it lives between the reader’s ears, and within the cultural milieu that interconnects the reading population over the generations — ''all as construed by the apparatus between the reader’s ears''.

This, by the way, is not in any way to diminish Shakespeare’s towering genius, but rather to observe that, however impossibly brilliant it is, it is swamped by the flood of exposition, analysis, interpretation, re-rendition and performance that has gone on since he published it, all as narratised and framed by the apparatus between the reader’s ears.

''Hamlet'' speaks, still, to the social and human dilemmas of the twenty-first century in ways Shakespeare cannot possibly have contemplated.<ref>A bit ironic that Microsoft should call its chatbot “Bard”, of all things.</ref>

A reader is no simple “symbol processor”, decrypting text to reveal a one-to-one assembly of the author’s intention in her own head. Literature is no instruction manual, recipe nor a computer programme.

Now there is one kind of “literature” that is like a computer programme: where the ''last'' thing the writer wants is for the reader use her imagination, to construct a meaning: where clarity of authorial intention is paramount; where communicating and understanding ''purpose'' is the sole priority: ''legal'' literature.  

Rather than ceding interpretative control to the reader, a legal drafter seeks to squash all opportunities for improvisation and stomp out all ambiguity. Just as there are no atheists in foxholes, [[there are no metaphors in a trust deed|there are no metaphors in a Trust Deed]].  

Legal drafting seeks to be as [[finite]] as it can be. It strives do to readers what code does to hardware: to reduce them to mere symbol-processing machines, extracting the author’s single incontrovertible meaning. But, in a natural language that is constructed out of dead metaphors, this is very, very hard to do. That there is such a living to be made conducting commercial litigation shows that.

It is one reason why [[legalese]] is so laboured. It is compelled to chase down all blind alleys, previsualise all phantoms and prescribe outcomes for all logical possibilities. To remove all possible ambiguity and render the text as mechanical, precise and reliable as it can be. ''[[There are no metaphors in a trust deed]]''. Where normal literature favours possibility over certainty, legal language bestows [[certainty]] at the cost of [[possibility]], and to hell with literary style and elegance.

Now: the punchline. Given how integral the reader and her cultural baggage are to the creative act in ''normal'' literature, we can see how, in that domain, a [[large learning model]], which spits out text ripe with interpretative possibilities, begging for someone to “construct” it, is a feasible model for that kind of language: to move from a model where ''most'' of the creative work is done by the reader to one where ''all'' of it is, is no great step.  

There is enough bad human literature in existence already, that is is no great stretch to imagine doing without the human altogether. Who cares what the text is ''meant'' to say, as long as it is coherent enough for an enterprising reader to make something out of it?

''But that does not work at all at all for legal language''. Legal language is code: it must say exactly what the parties require: nothing more or less, and it must do it in a way that leaves nothing open to a later creative act of interpretation. Legal drafting is as close to computer code as natural language gets: a form of symbol processing where the meaning resides wholly within and is fully limited by the text.  

But unlike computer code, the operating system it is written for is not a closed logical system, and even the best-laid code can still run amok. You can’t run it in a sandbox to see if it works. You have to test in production.

This is not to say that a [[large language model]] can’t be used to generate legal [[boilerplate]]: it just can’t do it by itself, and the process of working with it will be a lot more labour-intensive than the first round of generation suggests. An LLM will silt towards an intended meaning asymptotically, getting progressively less efficient as it goes.  

We have seen it suggested that one might invert the processes instead, and use the machine to critique drafts prepared by humans, to identify potential errors and omissions. But this is to get the [[division of labour]] exactly backwards, using expensive, context-sensitive [[meatware]] to do the legwork and a dumb machine to provide the “magic”.  

Without wishing to seem all John Connor about it, there is a question of basic human dignity going on here. We ''need to stop subordinating ourselves to machines''. We should not compromising just to optimise for machine processing, to make it easier for machines to manage. There is a question of self-belief — self-respect — here.  

And besides, there is a design flaw in any legal process which supposes that the risk in a legal contract is distributed evenly throughout its content, and that therefore the legal proposition is one of handling volume.  

Boilerplate is boilerplate for a reason. It is pinned down; done, it takes the goes without saying out of the equation. ''[[The quotidian is a utility, not an asset]]''. There is nothing to be gained from having a large language model drafting it from scratch each time. The important part of the legal drafting process is not assembling the boilerplate, but getting the deal-specific bespoke bits right.

====Meet the new boss —====

We don’t doubt that LLM is coming, nor that the legal industry will find a use for it: just that there is a ''useful'', sustained use for it. It feels more like a parlour trick: surprising at first, diverting after a while, but then the novelty wears off, and the appeal of persevering with what is basically a gabby but unfocussed child wears pales.

The traditional legal model faces existential challenges for sure, but they are not presented, and will not be addressed by random word generators.

==== Coda: is ChatGPT getting worse? ====

In other news, scientists are concerned that [[ChatGPT]] might be getting worse. Studies indicate that its accuracy at tasks requiring computational accuracy, like playing noughts and crosses or calculating prime numbers, is rapidly diminishing.