Pith. sign in

REVIEW 3 major objections 4 minor 90 references

Machines of Meaning

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper argues that meaning for an AI is a learned, goal-directed link between a symbol and its context, and that current large language models already satisfy this definition.

desk verdict A thoughtful conceptual essay on meaning and grounding whose central claim about LLMs being already machines of meaning does not survive contact with its own definitions. read the letter →

arxiv 2412.07975 v1 pith:HMHEH75L submitted 2024-12-10 cs.AI cs.CLcs.CYcs.LG

Davide Nunes , Luis Antunes This is my paper · ORCID
classification cs.AIcs.CLcs.CYcs.LG
keywords meaninggroundingsymbolproblemlargelanguagemodelsframecomputationalsemanticsartificialintelligencegames
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish a precise, non-anthropocentric definition of meaning for artificial agents, then argues that today's large language models already qualify as machines of meaning. It defines meaning as a learned connection between a symbol and its referent in a context, where the context matters because it serves a goal. Grounding, on this view, is the process by which an agent updates its world model by experiencing the contexts in which symbols are used, guided by its goals. From this perspective, many apparent failures of language models to understand come from misconceptions about what meaning requires, not from a missing human-like capacity. The paper then identifies two concrete architectural obstacles that prevent current models from adapting to open, evolving linguistic worlds: fixed lexicons and the need to output full probability distributions.

What carries the argument

The load-bearing machinery is the paper's three-part conceptual apparatus: the definition of a symbol as a behavioural pattern with no intrinsic meaning; the definition of grounding as goal-guided updating of a world model through contexts of use; and Definition 3, which makes meaning a learned, goal-directed symbol-to-context connection rather than propositional content. This apparatus is used to reject the claim that programs cannot understand, via the principle that a simulation of something can be that thing when the property in question depends only on functional organisation. It also introduces the 'prediction frame problem', the impossibility of fixing the lexicon and output space in advance, which the paper presents as the real obstacle separating current language models from machines of meaning that adapt to open linguistic worlds.

What would settle it

A concrete experiment: train two identical language models, one with a goal-dependent training signal such as human preference and one with pure next-token prediction, then measure for the same symbols whether each model's internal updates track goal relevance as Definition 2 requires. If the pure predictor already shows the same goal-relevant grounding, the goal clause in Definition 3 is superfluous; if the goal-trained model alone shows it, the paper's account is confirmed.

Watch

Extended reading notes

Core claim

The paper's central claim is that meaning, for an artificial agent, is the learned connection between a symbol and its referent in a context, where the features of that context are meaningful because they are important for a goal such as survival or coordination (Definition 3). Grounding is the process whereby an agent updates its world model by experiencing the contexts where a symbol is used, with relevance guided by the agent's goals (Definition 2). On these definitions, the paper argues, modern large language models already qualify as machines of meaning: they learn symbol-to-context associations from data and use them to pursue prediction and human-alignment goals. Their apparent failures to 'understand' stem not from a missing capacity but from a misconception about what meaning requires, namely that it must be human-like. The paper then identifies two architectural obstacles that prevent these models from being machines of meaning in open, evolving linguistic domains: fixed lexicons and the output of full probability distributions, and it proposes likelihood-free estimation and compressive encoding as candidate solutions.

Load-bearing premise

The load-bearing premise is that being a thing that understands depends only on how it is functionally organised, not on what it is made of; if that premise fails, the claim that language models can genuinely have meaning loses its footing.

Editorial extensions

If this is right

  • If the definitions are right, current LLMs can be said to possess machine-relative meaning without needing human-like embodiment or subjective experience.
  • The debate about LLM understanding shifts from 'can they mean at all' to 'what goals and contexts are their symbols grounded in'.
  • Scaling alone will not produce machines of meaning that keep pace with evolving language; architectures must allow incremental learning with open vocabularies.
  • Likelihood-free training and compressive symbol encodings become first-class research directions rather than merely practical tricks.
  • AI risk and capability assessment can be made more precise by evaluating goal-relevant grounding rather than anthropomorphic notions of understanding.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: If Definition 3 is taken literally, almost any goal-directed reinforcement learner qualifies as a machine of meaning for its input symbols, so the term becomes a general property of goal-directed representation learning rather than a specifically linguistic achievement.
  • Editorial inference: A direct test of the 'already machines of meaning' claim is to compare a pure next-token predictor with a goal-conditioned variant on the same corpus; only if the latter shows measurably different symbol-to-context structure does the goal clause carry explanatory weight.
  • Editorial inference: The paper's proposed fixes point to a concrete extension: build a small-scale model with a continually growing lexicon and test its ability to acquire new symbols online, which would operationalize 'machines of meaning' in a way current frozen-vocabulary benchmarks cannot.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper is a conceptual and philosophical essay that proposes definitions of symbol, grounding, and meaning for artificial agents. It argues against Searle's Chinese Room Argument by appealing to Chalmers' functionalist principle that a simulation of X can be an X when the property of being an X depends only on functional organization. The paper reviews the trajectory from structural linguistics and distributional semantics to modern neural language models, and then claims in Section 5 that modern language model implementations are already 'machines of meaning.' It identifies two major obstacles to their full potential: the reliance on fixed lexicons and the training objective of outputting full probability distributions. The proposed way forward combines compressive encoding (e.g., random projections) with likelihood-free estimation methods to enable incremental, open-vocabulary learning.

Significance. If the paper's central claim were established, it would reframe debates about whether LLMs have meaning and would focus attention on specific architectural obstacles. The paper's strengths are its clear separation of symbols, grounding, and meaning; its broad and relevant synthesis from philosophy of language through computational semantics to LLMs; and its explicit naming of the 'prediction frame problem' with a concrete research agenda. The paper does not provide empirical evaluations, machine-checked formalizations, or falsifiable predictions; its contribution is definitional and programmatic. This kind of conceptual work can be valuable for a field that often conflates model behavior with understanding, but the central assertion that current LLMs are already machines of meaning must be internally consistent and non-vacuous for the paper to be convincing.

major comments (3)
  1. [Section 5, first paragraph] The paper asserts 'modern implementations of language models are already machines of meaning', but Section 6 states that current approaches are 'lacking proper grounding mechanisms for language semantics', and Section 5.3 lists fixed lexicons and full output distributions as 'major obstacles to their full potential as MoMs'. Under Definition 2, grounding is the process of updating a world model by experiencing contexts where symbol use is relevant, guided by the agent's goals. A transformer trained on static text with a next-token objective does not update a world model during inference and has no goal-directed experience in the sense of Definition 2. If the assertion is read weakly, any n-gram model with a prediction objective already qualifies as a machine of meaning, which trivializes the claim; if read strongly, it contradicts the paper's own limitations. This equivocation is load-bearing and must be resolved.
  2. [Definitions 2 and 3] Definition 3 defines meaning as a learned connection between a symbol and its referent in a context, with relevance determined by a goal. The paper never gives an operational criterion for when a model's parameters instantiate such a learned connection, nor does it show that a current LLM's training objective (e.g., next-token prediction or RLHF reward) amounts to the goal-directed relevance required by Definition 2. Without such a criterion, the claim that current LLMs are machines of meaning is either vacuously true under a permissive reading of 'goal' or unsupported under a strict reading. The authors should state which reading they intend and justify it, especially because the conclusion explicitly denies that current approaches have proper grounding.
  3. [Section 3] The refutation of the Chinese Room Argument depends on Chalmers' principle that 'A simulation of X can be an X when the property of being an X depends only on the functional organization of the underlying system, and not on any other details.' This premise is asserted rather than defended; the artificial-heart and artificial-limb analogies are suggestive but do not establish the premise. Because this functionalist move is what licenses treating LLM computational states as potential carriers of meaning, the authors should either provide an argument for the premise or explicitly mark the claim as conditional on a contested philosophical position. As it stands, the central claim rests on an unargued assumption that many readers, including Searle and subsequent critics, will reject.
minor comments (4)
  1. [Section 3] There is a typo: 'Or arse they functional duplicates of hearts?' should read 'Or are they functional duplicates of hearts?'.
  2. [Section 4] The name 'Dennett' is misspelled as 'Dennet' in the discussion of the Twin Earth thought experiment.
  3. [References] Several references are incomplete or inconsistent in formatting; for example, [8], [12], [22], and [34] lack complete journal, volume, or publisher details. A journal submission should have full bibliographic entries.
  4. [Section 5.3] The 'prediction frame problem' is a useful concept, but its statement would be clearer with a formal or semi-formal definition, as currently it is described through examples rather than a precise problem formulation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: this is a conceptual essay whose claims are stipulative and self-contained, with only minor non-load-bearing self-citations.

full rationale

This paper does not exhibit any of the enumerated circularity patterns. It makes no empirical predictions, fits no parameters, and presents no mathematical derivation that could reduce to its own inputs. The central assertion that "modern implementations of language models are already machines of meaning" follows from the authors' openly stated Definition 3, which defines meaning as the learned, goal-relevant connection between a symbol and its context; language models learning next-token prediction can be described that way. That makes the claim a definitional application rather than an independent empirical result, but a stipulative definition is not a circular derivation when the definition is stated explicitly and the authors repeatedly warn against over-reading model capabilities. The self-citations are not load-bearing: reference [25] supports a definitional claim about grounding that is also argued from the paper's own philosophical premises, and reference [82] is offered only as a possible future research direction for random projections. Section 6's statement that current approaches are "lacking proper grounding mechanisms for language semantics" is in tension with Section 5's assertion that LLMs are already machines of meaning, but this is an internal equivocation or inconsistency, not the output being equivalent to the input by construction. No uniqueness theorem, no fitted-parameter prediction, and no ansatz smuggled in via self-citation were found. The paper is therefore not circular in the sense assessed here.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no physical or mathematical entities. The 'Machine of Meaning' is a conceptual category, not a postulated object with falsifiable consequences. The axioms listed are the main unproved premises on which the argument rests.

assumptions (5)
  • domain assumption Symbols carry no intrinsic meaning; meaning is always learned and grounded in the agent's experience.
    Core premise of the paper's account of symbols, stated in Section 2, Definition 1.
  • domain assumption Grounding requires causal connections to the environment, and relevance is guided by the agent's goals.
    Stated as Definition 2 in Section 3; it is an unproved definition that underpins the MoM framework.
  • domain assumption A simulation of X can be an X when the property of being an X depends only on the functional organization of the underlying system.
    Adopted from Chalmers and quoted in Section 3; used to dismiss Searle's Chinese Room argument.
  • ad hoc to paper Meaning is the learned connection between a symbol and its referent in a context, where the relevance of features is importance for a goal such as survival or coordination.
    Definition 3 in Section 4 is stipulated to support the paper's conclusions about LLMs and MoMs.
  • domain assumption Language norms arise from coordination problems, and meaning is private in the sense that norms are private, but socially constructed and reality-involving.
    Assumed in Section 4 based on Lewis's work on convention; this supports the normative account of meaning.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Machines of Meaning." pith.science (2026). https://pith.science/paper/HMHEH75L

@misc{pith2026241207975,
  author       = {Pith},
  title        = {Pith review of: Machines of Meaning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HMHEH75L}},
  note         = {Machine review of arXiv:2412.07975}
}
read the original abstract

One goal of Artificial Intelligence is to learn meaningful representations for natural language expressions, but what this entails is not always clear. A variety of new linguistic behaviours present themselves embodied as computers, enhanced humans, and collectives with various kinds of integration and communication. But to measure and understand the behaviours generated by such systems, we must clarify the language we use to talk about them. Computational models are often confused with the phenomena they try to model and shallow metaphors are used as justifications for (or to hype) the success of computational techniques on many tasks related to natural language; thus implying their progress toward human-level machine intelligence without ever clarifying what that means. This paper discusses the challenges in the specification of "machines of meaning", machines capable of acquiring meaningful semantics from natural language in order to achieve their goals. We characterize "meaning" in a computational setting, while highlighting the need for detachment from anthropocentrism in the study of the behaviour of machines of meaning. The pressing need to analyse AI risks and ethics requires a proper measurement of its capabilities which cannot be productively studied and explained while using ambiguous language. We propose a view of "meaning" to facilitate the discourse around approaches such as neural language models and help broaden the research perspectives for technology that facilitates dialogues between humans and machines.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

90 extracted references · 62 canonical work pages

  1. [1]

    In: Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, pp

    Bender, E.M., Koller, A.: Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data. In: Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, pp. 5185–5198. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.463 . https://aclanthology.org/2020.acl-main.463

    doi:10.18653/v1/2020.acl-main.463

  2. [2]

    In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, And Trans- parency

    Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the Dan- gers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, And Trans- parency. F AccT ’21, pp. 610–623. Association for Computing Machinery (2021). https://doi.org/10.1145/3442188.3445922 23

    arXiv 2021

  3. [3]

    49(3), 197–237 https: //doi.org/10.1037/h0055737

    Osgood, C.E.: The nature and measurement of meaning. 49(3), 197–237 https: //doi.org/10.1037/h0055737

    work page doi:10.1037/h0055737

  4. [4]

    Steels, L.: The symbol grounding problem has been solved, so what’s next? In: Symbols and Embodiment: Debates on Meaning and Cognition, pp. 223–244. Oxford University Press (2008). https://doi.org/10.1093/acprof:oso/ 9780199217274.003.0012

    doi:10.1093/acprof:oso/ 2008

  5. [5]

    Cambridge university press (1997)

    Clancey, W.J.: Situated Cognition: On Human Knowledge and Computer Repre- sentations. Cambridge university press (1997)

    1997

  6. [6]

    Peirce, C.S.: Collected Papers of Charles Sanders Peirce vol. 5. Harvard University Press (1974)

    1974

  7. [7]

    Cognitive Science 4(2), 135–183 (1980) https://doi.org/10.1207/s15516709cog0402 2

    Newell, A.: Physical symbol systems*. Cognitive Science 4(2), 135–183 (1980) https://doi.org/10.1207/s15516709cog0402 2

    work page doi:10.1207/s15516709cog0402 1980

  8. [8]

    1016/0167-2789(90)90087-6

    Harnad, S.: The symbol grounding problem 42(1), 335–346 https://doi.org/10. 1016/0167-2789(90)90087-6

Show all 90 references
  1. [9]

    In: Readings in Artificial Intelligence, pp

    McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. In: Readings in Artificial Intelligence, pp. 431–450. Elsevier. https://doi.org/10.1016/B978-0-934613-03-3.50033-7

    doi

  2. [10]

    The MIT Press (1980)

    Dennett, D.C.: Brainstorms: Philosophical Essays on Mind and Psychology. The MIT Press (1980)

    1980

  3. [11]

    In: Hookway, C

    Dennett, D.C.: Cognitive wheels: The frame problem of ai. In: Hookway, C. (ed.) Minds, Machines and Evolution. Cambridge University Press (1986)

    1986

  4. [12]

    In: Pylyshyn, Z.W

    Fodor, J.A.: Modules, frames, fridgeons, sleeping dogs, and the music of the spheres. In: Pylyshyn, Z.W. (ed.) The Robot’s Dilemma: the Frame Problem in Artificial Intelligence. Theoretical Issues in Cognitive Science, pp. 139–49. Holtzbrinck (1987)

    1987

  5. [13]

    Behavioral and Brain Sciences 3, 417– 424 (1980) https://doi.org/10.1017/S0140525X00005756

    Searle, J.R.: Minds, brains, and programs. Behavioral and Brain Sciences 3, 417– 424 (1980) https://doi.org/10.1017/S0140525X00005756

    1980 doi

  6. [14]

    Communications of the ACM 19(3), 113–126 (1976) https://doi.org/10

    Newell, A., Simon, H.A.: Computer science as empirical inquiry: Symbols and search. Communications of the ACM 19(3), 113–126 (1976) https://doi.org/10. 1145/360018.360022

    1976

  7. [15]

    Cambridge University Press (1988)

    Boden, M.A.: Computer Models of Mind: Computational Approaches in Theo- retical Psychology. Cambridge University Press (1988)

    1988

  8. [16]

    Philosophy of science 67(2), 177–194 (2000) 24

    Smith, J.M.: The concept of information in biology. Philosophy of science 67(2), 177–194 (2000) 24

    2000

  9. [17]

    Oxford University Press (1989)

    Penrose, R.: The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. Oxford University Press (1989)

    1989

  10. [18]

    Oxford University Press (1996)

    Chalmers, D.J.: The Conscious Mind: In Search of a Fundamental Theory. Oxford University Press (1996)

    1996

  11. [19]

    Journal of Experimental & The- oretical Artificial Intelligence 1(1), 5–25 (1989) https://doi.org/10.1080/ 09528138908953691

    Harnad, S.: Minds, machines and searle. Journal of Experimental & The- oretical Artificial Intelligence 1(1), 5–25 (1989) https://doi.org/10.1080/ 09528138908953691

    1989

  12. [20]

    Views into the Chinese Room, 95–108 (2002)

    Simon, H.A., Eisenstadt, S.A.: A chinese room that understands. Views into the Chinese Room, 95–108 (2002)

    2002

  13. [21]

    Artificial Intelligence and Neural Networks: Steps towards Principled Integration 1, 155–189 (1994)

    Sharkey, N.E.: Three horns of the representational trilemma. Artificial Intelligence and Neural Networks: Steps towards Principled Integration 1, 155–189 (1994)

    1994

  14. [22]

    In: Riegler, A., Peschl, M., Stein, A

    Ziemke, T.: Rethinking grounding. In: Riegler, A., Peschl, M., Stein, A. (eds.) Understanding Representation in the Cognitive Sciences, pp. 177–190. Springer. https://doi.org/10.1007/978-0-585-29605-0 20

    doi

  15. [23]

    Cognitive Systems Research 3(3), 429–457 (2002) https://doi.org/10.1016/S1389-0417(02)00051-7

    Vogt, P.: The physical symbol grounding problem. Cognitive Systems Research 3(3), 429–457 (2002) https://doi.org/10.1016/S1389-0417(02)00051-7

    2002 doi

  16. [24]

    Technical note 323 (1984)

    Nilsson, N.J.: Shakey the robot, ai center, sri international, menlo park, ca (us). Technical note 323 (1984)

    1984

  17. [25]

    Davide Nunes: The geometry of desire

    Luis Antunes, H.C. Davide Nunes: The geometry of desire. In: Lomuscio, A., Scerri, P., Bazzan, A., Huhns, M. (eds.) 13th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), pp. 1169–1172. IF AAMAS, Dallas, Texas (2014)

    2014

  18. [26]

    Brooks, R.A.: Intelligence without representation 47(1), 139–159 https://doi.org/ 10.1016/0004-3702(91)90053-M

    doi

  19. [27]

    In: Proceedings of the 12th Inter- national Joint Conference on Artificial Intelligence - Volume 1

    Brooks, R.A.: Intelligence without reason. In: Proceedings of the 12th Inter- national Joint Conference on Artificial Intelligence - Volume 1. IJCAI’91, pp. 569–595. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1991)

    1991

  20. [28]

    In: Mc Kevitt, P

    Jackson, S.A., Sharkey, N.E.: Grounding computational engines. In: Mc Kevitt, P. (ed.) Integration of Natural Language and Vision Processing: Theory and Grounding Representations vol. 3, pp. 167–184. Springer. https://doi.org/10. 1007/978-94-009-1639-5 13

  21. [29]

    tractatus logico-philosophicus

    Church, A.: Wittgenstein ludwig. tractatus logico-philosophicus . with an intro- duction by russell bertrand. fourth impression. routledge & kegan paul, london 1949, 189 pp. Journal of Symbolic Logic 15, 157–157 (1950)

    1950

  22. [30]

    Edited by Perry Meisel and Haun Saussy

    Saussure, F., Baskin, W.: Course in General Linguistics: Translated by Wade 25 Baskin. Edited by Perry Meisel and Haun Saussy. Columbia University Press (2011)

    2011

  23. [31]

    Psychology Press (1990)

    Harris, R.: Language, Saussure and Wittgenstein: How to Play Games with Words. Psychology Press (1990)

    1990

  24. [32]

    John Wiley & Sons (2010)

    Wittgenstein, L.: Philosophical Investigations. John Wiley & Sons (2010)

    2010

  25. [33]

    John Wiley & Sons (2008)

    Lewis, D.: Convention: A Philosophical Study. John Wiley & Sons (2008)

    2008

  26. [34]

    Publisher: Journal of Philosophy Inc

    Dennett, D.C.: Real patterns 88(1), 27–51 https://doi.org/10.2307/2027085 . Publisher: Journal of Philosophy Inc

    doi

  27. [35]

    Bennett, M.R., Hacker, P.M.S., Bennett, M.: Philosophical Foundations of Neuroscience vol. 79. Blackwell Oxford

  28. [36]

    Routledge (2009)

    Russell, B.: The Philosophy of Logical Atomism. Routledge (2009)

    2009

  29. [37]

    The philosophical review 57(3), 209–230 (1948)

    Frege, G.: Sense and reference. The philosophical review 57(3), 209–230 (1948)

    1948

  30. [38]

    Fodor, J.A.: The Language of Thought vol. 5. Harvard University Press

  31. [39]

    In: Cas- sone, V.I., Ponzo, J., Thibault, M

    Bianchi, A.: The language of thought as a logically perfect language. In: Cas- sone, V.I., Ponzo, J., Thibault, M. (eds.) Languagescapes: Ancient and Artificial Languages in Today’s Culture, pp. 159–168 (2020)

    2020

  32. [40]

    Behaviorism 5(1), 43–59 (1977)

    Waller, B.: Chomsky, wittgenstein, and the behaviorist perspective on language. Behaviorism 5(1), 43–59 (1977)

    1977

  33. [41]

    The Philosophical Review 64(3), 449–483 (1955)

    Feyerabend, P.: Wittgenstein’s philosophical investigations. The Philosophical Review 64(3), 449–483 (1955)

    1955

  34. [42]

    Harvard University Press (1982)

    Kripke, S.A.: Wittgenstein on Rules and Private Language: An Elementary Exposition. Harvard University Press (1982)

    1982

  35. [43]

    Animal Behaviour 28(4), 1070–1094 (1980) https://doi.org/10.1016/S0003-3472(80)80097-2

    Seyfarth, R.M., Cheney, D.L., Marler, P.: Vervet monkey alarm calls: Semantic communication in a free-ranging primate. Animal Behaviour 28(4), 1070–1094 (1980) https://doi.org/10.1016/S0003-3472(80)80097-2

    1980 doi

  36. [44]

    Language, Mind and Knowledge: Min- nesota Studies in the Philosophy of Science 7, 131–193 (1975)

    Putnam, H.: The meaning of meaning. Language, Mind and Knowledge: Min- nesota Studies in the Philosophy of Science 7, 131–193 (1975)

    1975

  37. [45]

    Behavioral and Brain Sciences 27(2), 169–190 (2004) https://doi.org/10.1017/ S0140525X04000056

    Pickering, M.J., Garrod, S.: Toward a mechanistic psychology of dialogue. Behavioral and Brain Sciences 27(2), 169–190 (2004) https://doi.org/10.1017/ S0140525X04000056

    2004

  38. [46]

    CSLI publications Stanford (2003) 26

    Francis, E., Michaelis, L.A.: Mismatch: Form-function Incongruity and the Architecture of Grammar. CSLI publications Stanford (2003) 26

    2003

  39. [47]

    Proceedings of the National Academy of Sciences 114(40), 10785–10790 (2017) https://doi.org/10.1073/pnas.1619666114

    Gibson, E., Futrell, R., Jara-Ettinger, J., Mahowald, K., Bergen, L., Ratnasingam, S., Gibson, M., Piantadosi, S.T., Conway, B.R.: Color naming across languages reflects color use. Proceedings of the National Academy of Sciences 114(40), 10785–10790 (2017) https://doi.org/10.1...

    2017 doi

  40. [48]

    Language 28(4), 474–494 (1952)

    Harris, Z.S.: Discourse analysis: A sample text. Language 28(4), 474–494 (1952). Accessed 2023-07-02

    1952

  41. [49]

    Word 10(2-3), 146–162 (1954)

    Harris, Z.S.: Distributional structure. Word 10(2-3), 146–162 (1954)

    1954

  42. [50]

    Studies in linguistic analysis, 10–32 (1957)

    Firth, J.: A synopsis of linguistic theory, 1930-1955. Studies in linguistic analysis, 10–32 (1957)

    1957

  43. [51]

    In: Proceedings of the Conference on Mechanical Translation (1952)

    Weaver, W.: Translation. In: Proceedings of the Conference on Mechanical Translation (1952)

    1952

  44. [52]

    Italian Journal of Disability Studies 20, 33–53 (2008)

    Sahlgren, M.: The distributional hypothesis. Italian Journal of Disability Studies 20, 33–53 (2008)

    2008

  45. [53]

    Journal of psycholinguistic research 13(6), 495–496 (1984)

    Landauer, T.K.: Statistical semantics-analysis of the potential performance of keyword information-systems, and a cure for an ancient problem. Journal of psycholinguistic research 13(6), 495–496 (1984)

    1984

  46. [54]

    Psychological review 104(2), 211 (1997)

    Landauer, T.K., Dumais, S.T.: A solution to plato’s problem: The latent seman- tic analysis theory of acquisition, induction, and representation of knowledge. Psychological review 104(2), 211 (1997)

    1997

  47. [55]

    Harper San Francisco (1999)

    Berners-Lee, T.: Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor. Harper San Francisco (1999)

    1999

  48. [56]

    Computational linguistics 18(4), 467– 480 (1992)

    Brown, P.F., Della Pietra, V.J., Desouza, P.V., Lai, J.C., Mercer, R.L.: Class- based n-gram models of natural language. Computational linguistics 18(4), 467– 480 (1992)

    1992

  49. [57]

    Journal of the American society for information science 41(6), 391–407 (1990)

    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American society for information science 41(6), 391–407 (1990)

    1990

  50. [58]

    arXiv preprint arXiv:1404.1100 (2014)

    Shlens, J.: A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100 (2014)

    2014 arXiv

  51. [59]

    Chomsky, N.: Linguistics and cognitive science: Problems and mysteries (1991)

    1991

  52. [60]

    Barlow, H.B.: Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1(4), 371–394 (1972)

    1972

  53. [61]

    Philosophical Transactions of the Royal Society B: Biological Sciences 27 369(1634), 20120392 (2014)

    Lambon Ralph, M.A.: Neurocognitive insights on conceptual knowledge and its breakdown. Philosophical Transactions of the Royal Society B: Biological Sciences 27 369(1634), 20120392 (2014)

    2014

  54. [62]

    In: Pro- ceedings of the Eighth Annual Conference of the Cognitive Science Society, vol

    Hinton, G.E., et al.: Learning distributed representations of concepts. In: Pro- ceedings of the Eighth Annual Conference of the Cognitive Science Society, vol. 1, p. 12 (1986). Amherst, MA

    1986

  55. [63]

    Neural computation 4(6), 863–879 (1992)

    Schmidhuber, J.: Learning factorial codes by predictability minimization. Neural computation 4(6), 863–879 (1992)

    1992

  56. [64]

    IEEE Transactions on Neural Networks 7(1), 142–146 (1996)

    Schmidhuber, J., Heil, S.: Sequential neural text compression. IEEE Transactions on Neural Networks 7(1), 142–146 (1996)

    1996

  57. [65]

    In: Leen, T., Dietterich, T., Tresp, V

    Bengio, Y., Ducharme, R., Vincent, P.: A neural probabilistic language model. In: Leen, T., Dietterich, T., Tresp, V. (eds.) Advances in Neural Information Processing Systems, vol. 13. MIT Press (2000)

    2000

  58. [66]

    In: Proceedings of the 2013 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pp

    Mikolov, T., Yih, W.-t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)

    2013

  59. [67]

    Advances in neural information processing systems 30 (2017)

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)

    2017

  60. [68]

    Advances in Neural Information Processing Systems 35, 27730–27744 (2022)

    Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022)

    2022

  61. [69]

    arXiv preprint arXiv:2303.03378 (2023)

    Driess, D., Xia, F., Sajjadi, M.S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al.: Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378 (2023)

    2023 arXiv

  62. [70]

    In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp

    Eisenstein, J.: What to do about bad language on the internet. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 359–369 (2013)

    2013

  63. [71]

    C Users Journal 12(2), 23–38 (1994)

    Gage, P.: A new algorithm for data compression. C Users Journal 12(2), 23–38 (1994)

    1994

  64. [72]

    Journal of Reading Behavior 27(2), 201–212 (1995)

    Zechmeister, E.B., Chronis, A.M., Cull, W.L., D’Anna, C.A., Healy, N.A.: Growth of a functionally important lexicon. Journal of Reading Behavior 27(2), 201–212 (1995)

    1995

  65. [73]

    Frontiers in psychology 7, 1116 (2016)

    Brysbaert, M., Stevens, M., Mandera, P., Keuleers, E.: How many words do we know? practical estimates of vocabulary size dependent on word definition, the 28 degree of language input and the participant’s age. Frontiers in psychology 7, 1116 (2016)

    2016

  66. [74]

    arXiv preprint arXiv:2302.13971 (2023)

    Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi` ere, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

    2023 arXiv

  67. [75]

    World Englishes 19(3), 305–322 (2000)

    Li, D.C.: Cantonese-english code-switching research in hong kong: A y2k review. World Englishes 19(3), 305–322 (2000)

    2000

  68. [76]

    Accessed 2023-08-05

    Ku, Y.-M., Anderson, R.C.: Development of morphological awareness in chinese and english 16(5), 399–422 https://doi.org/10.1023/A:1024227231216 . Accessed 2023-08-05

    2023 doi

  69. [77]

    In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp

    Chaudhary, A., Zhou, C., Levin, L., Neubig, G., Mortensen, D.R., Carbonell, J.: Adapting word embeddings to new languages with morphological and phonologi- cal subword representations. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, ...

    2018 doi

  70. [78]

    Artificial life 2(3), 319–332 (1995)

    Steels, L.: A self-organizing spatial vocabulary. Artificial life 2(3), 319–332 (1995)

    1995

  71. [79]

    Li, C., Flanigan, J.: Task Contamination: Language Models May Not Be Few-Shot Anymore. arXiv. http://arxiv.org/abs/2312.16337 Accessed 2024-01-11

    2024 arXiv

  72. [80]

    Accessed 2014-12-09

    Kanerva, P.: Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors 1(2), 139–159 https://doi.org/10.1007/s12559-009-9009-8 . Accessed 2014-12-09

    2014 doi

  73. [81]

    Journal of computer and System Sciences 66(4), 671–687 (2003)

    Achlioptas, D.: Database-friendly random projections: Johnson-lindenstrauss with binary coins. Journal of computer and System Sciences 66(4), 671–687 (2003)

    2003

  74. [82]

    arXiv preprint arXiv:1807.00930 (2018)

    Nunes, D., Antunes, L.: Neural random projections for language modelling. arXiv preprint arXiv:1807.00930 (2018)

    2018 arXiv

  75. [83]

    Artifi- cial intelligence 33(3), 379–412 (1987)

    Hanks, S., McDermott, D.: Nonmonotonic logic and temporal projection. Artifi- cial intelligence 33(3), 379–412 (1987)

    1987

  76. [84]

    Neural computation 14(8), 1771–1800 (2002)

    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural computation 14(8), 1771–1800 (2002)

    2002

  77. [85]

    Journal of Machine Learning Research 6(4) (2005)

    Hyv¨ arinen, A., Dayan, P.: Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research 6(4) (2005)

    2005

  78. [86]

    In: Proceedings of the Thirteenth 29 International Conference on Artificial Intelligence and Statistics, pp

    Gutmann, M., Hyv¨ arinen, A.: Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In: Proceedings of the Thirteenth 29 International Conference on Artificial Intelligence and Statistics, pp. 297–304 (2010). JMLR Workshop and Conference...

    2010

  79. [87]

    In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol

    Kanerva, P., Kristoferson, J., Holst, A.: Random indexing of text samples for latent semantic analysis. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 22 (2000)

    2000

  80. [88]

    https://arxiv.org/abs/2410.01201

    Feng, L., Tung, F., Ahmed, M.O., Bengio, Y., Hajimirsadegh, H.: Were RNNs All We Needed? (2024). https://arxiv.org/abs/2410.01201

    2024 arXiv

  81. [89]

    In: Korhonen, A., Traum, D.R., M` arquez, L

    Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. In: Korhonen, A., Traum, D.R., M` arquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Lin- guistics, ACL 2019, Florence, Italy, July 28- Aug...

    2019 doi

  82. [90]

    Neuro- computing 599, 128096 (2024) https://doi.org/10.1016/j.neucom.2024.128096 30

    Bol´ on-Canedo, V., Mor´ an-Fern´ andez, L., Cancela, B., Alonso-Betanzos, A.: A review of green artificial intelligence: Towards a more sustainable future. Neuro- computing 599, 128096 (2024) https://doi.org/10.1016/j.neucom.2024.128096 30

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.