On General Language Understanding
Authors: David Schlangen
Venue: EMNLP 2023 Findings (2023)
Keywords: NLU
evaluation
benchmarking
semantics
dialogue
modelling
measurement
TL;DR: There is more to Natural Language Understanding than abstract text; and there is more to understanding than showing symptoms

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.

institutional On General Language Understanding
David Schlangen
Computational Linguistics / Department of Linguistics
University of Potsdam, Germany
david. www.laageeeennnototsoooeee
Abstract
Inw
personal letter writing
institutional writing
Natural Language Processing prides itself to be an impirically-mindee if not outright em- piricist field, and yet lately it seems get it- self into essentialist debates on issues of mean- ing measurement ("Do Large Language Models Understand Language, And If So, How Much?"). This is not by accident: Here, as everywhere, the evidence underspecifies the un- derstanding. As remedy, this paper sketches the outlines of model of understanding, which can ground questions of the adequacy of cur- rent methods of measurement of model quality. The paper makes three claims: A) That differ- ent language situation types have different characteristics, B) That language understand- ing multifaceted phenomenon, bringing together individualistic and social processes, and C) That the choice of Understanding Indi- cator marks the limits of benchmarking, and the beginnings of considerations of the ethics NLP use.
iimeedaayy
inteructiiity
familial face ta-face int.
face-to-fad int
bigh
low
familiariity andience design/ shared context
Figure I: A Space of Language Use Situation Types
section dives into the general part of GLU, Sec- tion 3 into the understanding, as cognitive pro- cess. Section zooms out, and looks at conditions under which model of GLU ceases to be just a model. In the course of the discussion, will de- rive three desiderata for models of GLU and their evaluation.
2 Types of Language Use
1 Introduction
Language be used for many purposes (c.g., dering dinner, teaching, making small talk with friends) and via various types of media (c.g., let- ters, computerised text messages, face-to-face), As Fillmore (1981, 152) observed, one setting appears to be primary, however: "The language 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-dimennional categorisa- tion of these deviations can be found in (Clark and Brennan, 1991; Clark, 1996); for our purposes here, this can be collapsed into two dimensions. as 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
In early 2019, Wang et al. (2019b) released the "General Language Understanding Evalua- tion" (GLUE) benchmark, with an updated ver- sion (Wang et al., 2019a) following only 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.' 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 (Srivastava al., 2022; Liang et al., 2022).
In this paper, I try to take step back and ask what "General Language Understanding" (GLU)
implemented in machines could mean. The next
At 91.3, compared to the 89.8 in the paper; https: //super, gluebenchmark. cow/leaderboaa last accessed 2023-06-09.
Where these purposes all come with their specific con- stitutive constraints on the language use, see e.g. (Bakhtin, 1986), (Wittgenstein, 1953/84, $23)).

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writing or recorded messages). What are the conse- quences of the changes? The increase in mediation comes with a loss of immediacy, which reduces opportunities of the addressee to influence the for- mulation 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 larger contiguous message, and not when language is used to guide collabora- tive 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 (Miller and Weinert, 1998; Halliday, 1989). Dif- ferences can also be found in the range of its func- tions: the range of speech acts that can be found in high-interactivity settings is larger, and includes all kinds of interaction management acts (Asher and Lascarides, 2003; Ginzburg, 2012; Bunt, 1996), 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 rich shared situational context, to use be- tween speakers who do not. Consequently, the kind of background information that the speakers can presuppose changes, leading to need 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 "under- standing work", at least if the language production is good, has to be "front-loaded" by the language producer, who cannot rely on the addresse interven- ing (bottom row) or the availability of much shared context (left column).
anything, but it worth do it, the fact that the more abstract types of lan- guage production found in the top-right quadrant come less easy to them may indicate that the meth- ods 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.)
humans
Second, can note that, as consequence of this development trajectory, all of the extant large scale, "general" evaluation efforts (Srivastava al., 2022; Liang et al., 2022) target this top-right quad- rant. 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 ggenda-forming function of bench- marks, this is concerning for the emergence of a field of true GLU. (We might term this the cover- age 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 well abstracted language use.
3 Understanding as a Cognitive Process: Inside the Understander
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" (Allen, 1995, p.4).
We can use this diagram to make several ob- servations. First, while the ontogenetic trajec- tory takes the human language learner from the strongest kind of the "basic and primary" form of language- naanely child/caretaker face-to-far in- teraction (Clark, (003)-outwand which some are only accessible via formal educa- tion (writing in general, then technical/scientific writing), the trajectory for Natural Language Un- derstanding in NLP takes the exact opposite direc- tion, only now moving from the top-left corner of processing formal writing further towards the ori- gin (Bisk et al., 2020).3 3 This does not have to mean
Taking up this "actionable representation" view, and at first focussing "text understanding". Fig- ure 2 (left column) shows attempt com- pile out of the vast literature on language un- derstanding, both from NLP, but also from lin- guistics and psycholinguistics, a general (if very schematic) picture a picture that at this level of de- tail would not be incomprehensible to the contem-
into regions of
porary reader of Allen (1995). The model assumes that the language understander possesses a model
"Note that while there is renewed interest in "embodied" language use in NLP (Duan et al., 2022; Gu et al., 2022). outside of the small interactions with the neighbouring field of social roboties, there is little work on actual embodiment that could lead to models of "facc-to-face" interaction.
'For evidence that the increase in interactivity is inconsis- tent, see (Doğruöz and Skantze, 2021). A theoretical proposal for an evaluation method is given by (Schlangen, 2023), with a first realisation attempted by (Chalamalasetti et al., 2023).

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of the language in which the material that is to be understood is formulated; here in the more narrow sense that a model of a mapping between form and meaning (representation), roughly of the scope aimed at by the formalisations such as those of Chomsky (1957) or Pollard and Sag (1994).² This model interfaces with world knowledge at least in the lexicon (Pustejovsky and Batiukova, 2019), via knowledge of concepts (Murphy, 2002; Margolis and Laurence, 2015). 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.; Allen and Litman (1990); Nunberg (1987)).
language model
incremental
learning
world model
multimodal grounding
situation model
incremental processing
discourse model
conversational grounding
agent model saff
partour
Figure 2: A Model of Understanding as Cognitive Process
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 situa- tion; Johnson-Laird (1983); van Dijk and Kintsch (1983)). To give an example, Winograd schema sentences (Levesque et al., 2012) such as (1) (taken from (Wang et al., 2019a)) can in this scheme be un- derstood as inducing situation model, for which language and world model suggest a preferred un- derstanding (namely, that it was the table, as the patient of the breaking event, that was built out of the fragile material).
models, for example in the diagnostic dataset in- cluded in SuperGLUE (Wang al., 2019a), or in the checklists of Ribeiro et al. (2020). This as- sumption also underlies the fertile field of repre- sentation probing (e.g., (Marvin and Linzen, 2018; Belinkov, 2022; Loáiciga et al., 2022; Schuster and Linzen, 2022)), which tests for mapping between such theoretically motivated assumptions and em- pirical findings on processing models. However, the underlying assumptions are rarely made ex- plicit, not even the degree that it is done here (but see (Trott et al., 2020; Dunietz al., 2020)) which, want to claim here, should be done, the interest of construct validity measurement (Flake and Fried, 2020).
(1)
The large ball crashed right through the ta- ble because it was made of styrofoam.
not done. What have described far may capture text understanding, but once we move outwards from the top-right quadrant of Fig- ure 1, the collaborative nature interaction, and with it the importance of the agent model; and general the processual nature, and with it the im- portance of the various anchoring processes shown in Figure 2 on the right, come into focus. In order: Where it might be possible to understand text, par- ticularly of the de-contextualised kind described above, without reference its author, the further one moves towards the origin of the language use space (Figure 1), the clearer becomes that the understander needs to represent its beliefs about the interacting agent. This is indicated by the agent model in Figure 2, where the segment for the part- contains information about which parts of the other models the understander deems to be shared (Bratman, 1987; Cohen et al., 1990). The model of Clark (1 (1996) makes building up this common ground the point understanding, and the process of managing this common ground via interaction,
But
To be able separate between elements of the situation model that may have been implied and those that have been explicitly mentioned, repre- sentation of the discourse required (Heim, 1983; Kamp, 1981). Its structure moreover can constrain what can be inferred, as in (2-a), where 'car' is not available as antecedent for the pronoun.
(2) a. The nearly bankrupt company did not own a car. It was on the verge of col- lapse.
b. The nearly bankrupt company did own car. was on the verge of collapse.
(We will skip the agent model for now.) That "language understanding" is internally structured and draws on various types of knowl- edge is implicitly acknowledged also in modern attempts at evaluating the performance of NLU
*Which is not to imply that an implemented language understanding system should be hased on such formalisations.

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is indeed how typical evaluation of such a system works: The response is compared the known, expected response, and the assumed quality of the model is a function of this comparison. This, how- ever, is not how understanding in real use situations works: Here, we do not care about understanding as sympiom (reflecting an inner state), but rather as understanding signal (offering social com- mitment). In most use situations, "computer says no" (Britain, 2004) not good enough (or least, should not be good enough): We want to know why says this, and we want to know who takes re- sponsibility if the reasons are found to be not good enough. 6 In other words, and as indicated in Fig- ure 3, in this view, the understanding indicator is embedded in practices of receiving challenges and providing justifications (Toulmin, 2003 [1958]), as well as making commitments (Brandom, 1998; Lascarides and Asher, 2009); in other words, it un- derlies the constraints holding for the speech act of assertion (Goldberg, 2015; Williamson, 2000).
stimulus
response
language under stander
challenge
justification
language
commitument
Figure 3: A Model of Understanding as a Social Process
conversational grounding, its central element. In conversational grounding, not only processes of repair (asking for clarification) are subsumed, but also "positive" indicators of understanding (such as producing relevant continuation). This process is made possible by the fact that processing of mate- rial happens, very much unlike the current assump- tions NLP, an incremental fashion (Levinson, 2010; Christiansen and Chater, 2016; Schlangen and Skantze, 2009), allowing for timely adaptations and interventions. Another natural phenomenon in interaction is covered by the process of incre- mental learning (Hoppit and Laland, 2013; Harris, 2015): If, in the course of interaction, am introduced to fact previously unknown me, and accept through conversational grounding, am expected able to later draw on it. The final process the only one that has seen some attention recently in NLP, multimodal grounding; which here however is meant not just to cover the word-world relation (Harnad, 1990; Chandu et al., 2021), but also the grounding of meaning- makinng in face-to-face situations, in multimodal signals from the speaker (Holler and Levinson, 2019; Mc- Neill, 1992; Kendon, 2004).
can only scratch the surface of this discussion here, to make a few notes: A) The target of "normal" evaluation-improviinn the reliability of the under- standing symptoms-certainly stands in some rela- tion to improving the quality of the understanding signal, but it is not entirely straightforward to what this relation is, and what its limits are. B) While the process of giving justifications when challenged may be within the range of "normal" work in NLP, and indeed is addresses by some work in "explainable Al" (Miller, 2019), whether the no- tion of commitment can ever be abstracted away from human involvement is more than questionable. C) There is long tradition of work making similar points, coming to them from different angle (e.g., inter alia, Bender et al. (2021)). I just note here that considerations originating from the philosophy of language about how meaning is underwritten by the " "game of giving and asking for reasons" (Sellars, 1956) only strengthen these concerns.
The takeaway from this shall be our second desideratum.
Desideratum 2: Attempts at measuring perfor- mance in "General Language Understanding" must be clear about their assumptions about the underlying construct.
(Where the model sketched in this section provides one example of how to be explicit about such as- sumptions.)
Desideratum 3: Uses of models of Language Un- derstanding must be clear about their understand- ing of the Understanding Indicator, and how it is warranted.
4 Understanding as Social Process: The Understanding Indicator
The discussion from the previous section suggests a picture where language understanding system receives a stimulus and delivers a response, which we take as an indicator understanding. And this
"A right which famously recent EU regulation (Regulation. 2016, recital 71, "right to explanation") indeed codifies, at least in principle.

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5 Conclusions
Emily M. Bender, Timnit Gebru, Angelina McMillan- Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language mod- els be too big? In Proceedings the 2021 ACM Conference on Fairmess, Accountability and Trans- parency, FAccT ',1, page 310-623, New York, NY, USA. Association for Computing Machinery.
This short paper is an invitation to debate what the meaning of "general language understanding" (in machines) could, and ought to, be. Ultimately, may be that the answer to "can large language mod- els model language understanding" is yes, while the answer to "can large language models understand language" has to be no.
Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lap- ata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, and Joseph Turian. 2020. Experience grounds language. EMNLP 2020 2020 Conference on Empirical Methods in Natural Lan- guage Processing, Proceedings of the Conference. pages 8718-8735.
Limitations
This paper does not report on any empirical work. Is it hence out of place at a conference on "empir- ical methods"? I would argue that it is not, as, in the words of Dewey (1960, p.85) "all experiment involves regulated activity directed by ideas". To not be empty, empirical methods must be guided by theoretical considerations, and it is this that this paper wants to contribute.
Robert Brandom. 1998. Making it Explicit: Reasoning, Representing, and Discursive Commitment. Harvard University Press, Harvard, MA, USA.
Michael E. Bratman. 1987. Intentions, Plans, And Prac- tical Reason. Harvard University Press, Cambridge. Massachusetts, USA.
A limitation might be that this text was written with the thought that language understanding by machines is done for humans, and that thus the of the understanding is crucial, be- cause only it guarantees that generalisations go in expected directions. The thoughts developed here might not apply if the goal is to evolve machine communication that only superficially resembles natural language.
Little Britain. 2004. Computer says no. British Broad- casting Company.
Harry Bunt. 1996. Interaction management functions and context representation reqirements. In Dialogue Management in NL Systems: Proceedings of the 11th Twente WS on Language Technology, pages 187-198, Enschede, The Netherlands.
Kranti Chalamalasetti, Jana Götze, Sherzod Haki- mov, Brielen Madureira, Philipp Sadler, and David Schlangen. 2023. clembench: Using game play evaluate chat-optimized language models as conver- sational agents. In Proceedings of the 2023 Confer- ence on Empirical Methods in Natural Language Pro- cessing, Singapore. Association for Computational Linguisties.
Ethics Statement
While the paper discusses some possible limits to language understanding by machines, it does not per question whether "general language under- standing" by machines is worthwhile, ethical goal. This should be discussed; for now, elsewhere.
Khyathi Raghavi Chandu, Yonatan Bisk, and Alan W Black. 2021. Grounding "Grounding" in NLP. Findings of ACI-IICNLP 2021.
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Timothy Williamson. 2000. Knowledge and its Limits. Oxford University Press.
Ludwig Wittgenstein. 1953/84, Tractatus Logicus Philosophicus und Philosophische Untersuchungen, volume of Werkausgabe. Suhrkamp. Frankfurt Main.


Reviews:
[{"review_id": "DIV8smEJzK", "reviewer": "Reviewer_uYnW", "summary": "", "questions": "None. ", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "There's probably not much I can do to change reviewer uYnW's mind. I shall nevertheless try to address some of the issues raised.\n\n> The contribution seems misplaced in a conference on Empirical Methods. Though this problem is addressed proactively as part of the paper, making the point that empirical enterprise also needs background thinking, the argumentation in the paper is not convincing.\n\nThere is an argument in the paper that foundational considerations do belong in a conference on empirical methods, especially if -- and this diagnosis is implied by the paper, and seems fairly common in the field -- the enthusiastic application of these methods has lead to a crisis of understanding, as evidenced by the discussion (also carried forward at EMNLP) of whether our empirical methods are aiming at and measuring the right things. Is this what uYnW is disagreeing with?\n\nThere is also an argumentation in the paper about how to remedy this. I would welcome engagement with this argumentation, to see where it is deemed unconvincing.\n\n\n> The paper reviews classics of symbolic AI and their claims (nothing new here)\n\nCan I respectfully point out that most of the cited papers are from linguistics, not \"symbolic AI\"? Furthermore, I would also like to stress -- and thank the reviewer for pointing out the need to clarify this yet more in the paper -- that the argument is for clarity on the underlying construct, the *what*, and is not aiming to provide strictures on the *how* of modelling the phenomenon. That is, the contrast between \"symbolic\" and \"sub-symbolic AI\" is orthogonal to the argument of the paper. The analytical observations of linguistics have not become irrelevant, just because the computational modelling methods may have changed.\n\n\n> does not address literature that is relevant particularly with respect to sub-symbolic AI. This includes Pearl's Ladder of Causation as well as philosophical thoughts about machine agency, as pursued for example, by Thomas Mueller.\n\nThank you for these interesting pointers to additional literature, some of which I indeed had not seen before. Again, this literature seems to be more about *how* to model, and not about *what* to measure. It seems to me that reviewer uYnW thinks that they have a better proposal. This may very well be! I would like to see that proposal being given the same room to be developed and presented as I am asking to be given here.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "N/A", "reasons_to_reject": "The contribution seems misplaced in a conference on Empirical Methods.  Though this problem is addressed proactively as part of the paper, making the point that empirical enterprise also needs background thinking, the argumentation in the paper is not convincing.  The paper reviews classics of symbolic AI and their claims (nothing new here) but does not address literature that is relevant particularly with respect to sub-symbolic AI.  This includes Pearl's Ladder of Causation as well as philosophical thoughts about machine agency, as pursued for example, by Thomas Mueller. ", "questions_to_authors": "None. ", "paper_topic_and_main_contributions": "This paper takes a philosophical stance and aims to develop a model of General Language Understanding by which LLMs can be evaluated. ", "excitement": 2, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "H. J. Briegel. On creative machines and the physical origins of freedom. Scientific Reports, 2:522, 2012.\n\nJ. Pearl. Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge, 2000.\n\nJ. Pearl. The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3), 2019.\n\nJ. Pearl and D. Mackenzie. The Book of Why. Basic Books, New York, 2018.\n\nK. Ried, B. Eva, T. Mueller, and H. J. Briegel. How a minimal learning agent can infer the existence of unobserved variables in\na complex environment. CoRR, abs/1910.06985196, 2019."}}, {"review_id": "Q61RfKfMsj", "reviewer": "Reviewer_tfyP", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "I thank reviewer tfyP for their receptive and encouraging comments and for their willingness to engage with the argument given here.\n\nI agree with the comment on the stark compression of the argumentation and the references to the literature. Should the paper be accepted, I will use the additional page to decompress the text a bit and add illustrative examples.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The claim, to present the above outline in a short paper, is justified by surveying extensive literature, including examples from the Winograd Schema collection (of difficult text for testing AI to see how human-like it can be). Language understanding quality is an apparent bottleneck of currently predominant big-data language models. Consequently, the proposed outline can, in my opinion, draw more community attention to the following: language use in conversation and language acquisition; ways the language structure can convey the role in a situation, and common sense reasoning based upon those roles; transparency of machine reasoning. In other words, the paper can draw attention to what is apparently missing from the current NLP, but could remedy its bottleneck problem.", "reasons_to_reject": "The information in the paper often comes in quintessence chunks. I mean that the paper often summarizes, say, some extensive research in a short phrase or sentence. To make the most of it, the reader needs to be well familiar with all the works summarized, otherwise it is hard to follow. I, for one, would befit more from straightforward explanations and technical examples of how the discussed understanding is implementable in real life. Considering the paper format and claim, my personal benefit comments have more to do with \"Excitement\".", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper contributes theoretical thought regarding the empirical method of using machinery to understand natural language. The author's position on \"General Language Understanding (GLU)\", in my opinion, closely relates to Artificial General Intelligence (AGI), an area concerned with deeper understanding by implementing human-like AI. The author proposes an outline of GLU issues to be addressed and invites the community to debate those issues. The outline deals with three aspects termed \"types [or purpose] of language use\", \"understanding as a cognitive process\", and \"understanding as a social process\".", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Jrq4GYdEw7", "reviewer": "Reviewer_kY1L", "summary": "", "questions": "Question A:\nCould deseridatum 2 be rewritten to be standalone rather than referring to “a model such as sketched here”? This seems a bit clunky to me.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "I thank reviewer kY1L for their encouraging reaction to the paper and their willingness to see relevance for our joint scientific undertaking in the field of NLP.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This is an extremely interesting paper on a timely topic as there has recently been much discussion—both among researchers and the general public/media/policy makers—of the extent to which language models ‘understand’ language.\n\nThe paper is very well structured, written, and argued.", "reasons_to_reject": "I agree with the author’s self-assessment in the ‘Limitations’ section that this work belongs at a venue like EMNLP, and see no reasons to reject the paper.", "questions_to_authors": "Question A:\nCould deseridatum 2 be rewritten to be standalone rather than referring to “a model such as sketched here”? This seems a bit clunky to me.", "paper_topic_and_main_contributions": "This position paper tackles the question of the extent to which language models can be said to ‘understand’ language.\nThe author provides an analysis of general language inderstanding on two dimensions: interactivity and familiarity. They propose that, placed on this matrix, the forms of communication most natural and easy for humans are quite different from those most easily evaluated in language models.\nThe author proposes three desiderata for modelling understanding in language models and concludes that while such modelling is possible, it cannot be said that language models display ‘understanding’.", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "l.227: “In oder:”"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a theoretical contribution on the highly relevant and timely topic of natural language understanding, in the light of recent technological developments. A few pointers to the literature have been suggested to better position the work in the context of EMNLP. Some definitions and theoretical principles may be further expanded to improve the readability and self-containment of the paper. 
While different from the "usual" contribution, this short paper has the potential to spark interesting and important discussions.
Decision: Accept-Findings