On General Language Understanding (2023)

Abstract: Natural Language Processing prides itself to be an empirically-minded, if not outright empiricist field, and yet lately it seems to get itself into essentialist debates on issues of meaning and measurement (“Do Large Language Models Understand Language, And If So, How Much?”). This is not by accident: Here, as everywhere, the evidence underspecifies the understanding. As a remedy, this paper sketches the outlines of a model of understanding, which can ground questions of the adequacy of current methods of measurement of model quality. The paper makes three claims: A) That different language use situation types have different characteristics, B) That language understanding is a multifaceted phenomenon, bringing together individualistic and social processes, and C) That the choice of Understanding Indicator marks the limits of benchmarking, and the beginnings of considerations of the ethics of NLP use.

## introduction in early 2019, @xcite released the "general language understanding evaluation" (glue) benchmark, with an updated version @xcite following only a little later. currently, the best performing model on the updated version sits comfortably above what the authors calculated as "human performance" on the included tasks. 1 this can mean one of two things: either general language understanding in machines has been realised, or these benchmarks failed to capture the breadth of what it constitutes. the consensus now seems to be that it is the latter @xcite . in this paper, i try to take a step back and ask what "general language understanding" (glu) implemented in machines could mean. the next 1 at 91.3, compared to the 89.8 in the paper; https: //super.gluebenchmark.com/leaderboard , last accessed 2023-06-09. section dives into the general part of glu, section 3 into the understanding, as a cognitive process. section 4 zooms out, and looks at conditions under which a model of glu ceases to be just a model. in the course of the discussion, i will derive three desiderata for models of glu and their evaluation. ## types of language use language can be used for many purposes (e.g., ordering dinner, teaching, making small talk with friends) and via various types of media (e.g., letters, computerised text messages, face-to-face). 2 as @xcite observed, one setting appears to be primary, however: "the language of face-to-face conversation is the basic and primary use of language, all others being best described in terms of their manner of deviation from that base." a detailed, multi-dimensional categorisation of these deviations can be found in @xcite ; for our purposes here, this can be collapsed into two dimensions, as in figure 1 . along the vertical axis, we move from high interactivity as it can be found in live interaction, to low-or non-interactive language use, as it is made possible by technical mediation (via writing or recorded messages). what are the consequences of the changes? the increase in mediation comes with a loss of immediacy, which reduces opportunities of the addressee to influence the formulation of the message, or in general to contibute. consequently, low-immediacy use situations are appropriate more when it is one language producer who wants to convey a larger contiguous message, and not when language is used to guide collaborative action. it is reasonable to expect this difference to have an effect on the form of the language that is produced, and indeed this is what is typically found @xcite . differences can also be found in the range of its functions: the range of speech acts that can be found in high-interactivity settings is larger, and includes all kinds of interaction management acts @xcite @xcite , the understanding of which requires reference to the state of the interaction. on the horizontal axis, we move from language use between speakers who share an extensive history of previous interactions and/or a rich shared situational context, to use between speakers who do not. consequently, the kind of background information that the speakers can presuppose changes, leading to a need to make much more of the presuppositions explicit in the "low shared context" setting. this leaves us with a quadrant (top-right) where a lot of the "understanding work", at least if the language production is good, has to be "front-loaded" by the language producer, who cannot rely on the addresse intervening (bottom row) or the availability of much shared context (left column). we can use this diagram to make several observations. first, while the ontogenetic trajectory takes the human language learner from the strongest kind of the "basic and primary" form of language-namely child/caretaker face-to-face interaction @xcite -outwards into regions of which some are only accessible via formal education (writing in general, then technical/scientific writing), the trajectory for natural language understanding in nlp takes the exact opposite direction, only now moving from the top-left corner of processing formal writing further towards the origin (bisk et al., 2020). 3 this does not have to mean anything, but it is worth noting-however humans do it, the fact that the more abstract types of language production found in the top-right quadrant come less easy to them may indicate that the methods that humans use to process language are taxed harder by them. (we might term the question of whether this is an essential or incidental feature the acquisition puzzle.) second, we can note that, as a consequence of this development trajectory, all of the extant large scale, "general" evaluation efforts @xcite target this top-right quadrant. no standard methods have yet been proposed for evaluating models that increase interactivity and/or context dependency. 4 this might be due to the factor that an increase in context-dependence requires concrete, and hence, less general setups; but given the agenda-forming function of benchmarks, this is concerning for the emergence of a field of true glu. (we might term this the coverage problem.) we derive from this discussion the first desideratum. desideratum 1: models of "general language understanding" must be general also with respect to language use situation types, and must cover situated as well as abstracted language use. the previous section looked at generality in terms of coverage of language use situations. this section will look at one aspect of the understanding part in "general language understanding". what is understanding? the classic view in nlp is well represented by this quote from a seminal textbook: "[the understanding system] must compute some representation of the information that can be used for later inference" @xcite ). taking up this "actionable representation" view, and at first focussing on "text understanding", figure 2 (left column) shows an attempt to compile out of the vast literature on language understanding, both from nlp, but also from linguistics and psycholinguistics, a general (if very schematic) picture-a picture that at this level of detail would not be incomprehensible to the contemporary reader of @xcite . the model assumes that the language understander possesses a model of the language in which the material that is to be understood is formulated; here in the more narrow sense that it is a model of a mapping between form and meaning (representation), roughly of the scope aimed at by the formalisations such as those of @xcite or @xcite . 5 this model interfaces with world knowledge at least in the lexicon @xcite , via knowledge of concepts @xcite . the world model, however, in this view more generally needs also to contain "common sense knowledge" about the workings of the world (e.g., as script knowledge, common sense physics, etc.; @xcite ; @xcite ). the central representation however in this model is the situation model representing the described situation, in the broadest sense (which may or may not be congruent with the reporting situation; @xcite ; van @xcite ). to give an example, winograd schema sentences @xcite such as (1) (taken from @xcite )) can in this scheme be understood as inducing a situation model, for which language and world model suggest a preferred understanding (namely, that it was the table, as the patient of the breaking event, that was built out of the fragile material). the large ball crashed right through the table because it was made of styrofoam. to be able to separate between elements of the situation model that may have been implied and those that have been explicitly mentioned, a representation of the discourse is required @xcite . its structure moreover can constrain what can be inferred, as in (2-a), where 'car' is not available as antecedent for the pronoun. ## conclusions this short paper is an invitation to debate what the meaning of "general language understanding" (in machines) could, and ought to, be. ultimately, it may be that the answer to "can large language models model language understanding" is yes, while the answer to "can large language models understand language" has to be no.