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arxiv: 2606.01671 · v1 · pith:V6IF7NRXnew · submitted 2026-06-01 · 💻 cs.CL

When Meaning Travels: A Granular Lens on Hybrid-MoE's Role in Idiomatic Understanding for Language Models

Sarmistha Das , Vaibhav Vishal , Shreyas Guha , Amaan Ali , Kitsuchart Pasupa , Sriparna Saha This is my paper

Pith reviewed 2026-06-28 15:15 UTC · model grok-4.3

classification 💻 cs.CL
keywords HybridMoEidiomatic understandingmultilingual multimodalfigurative languageSoutheast Asian languagesmixture of expertsVarnika corpus
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The pith

HybridMoE improves figurative language handling in multilingual vision models by integrating expert outputs and idiomatic signals.

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

The paper seeks to establish that a Hybrid Mixture-of-Experts model can better retain cultural and figurative meanings in idioms from low-resource languages by combining contributions from both chosen and unchosen experts while adding masked multimodal signals. This approach addresses expert sparsity in standard mixture models and is tested on a new corpus of Southeast Asian idioms paired with visual representations and seven tonal categories. A three-stage evaluation measures translation accuracy, visual alignment, and meaning preservation, with reported gains of 5 to 6 percent over baseline vision-language models. If the claim holds, it points to a practical way to embed cultural semantics without requiring entirely new model architectures.

Core claim

The HybridMoE framework embeds multiple idiomatic expert opinions while mitigating expert sparsity by integrating outputs from both selected and unselected experts through controlled hybridization, further augmented with Idiomatic Property Signals via masked multimodal embeddings, yielding 5--6% gains on IDIO-TONE and Idiomatic Validation Score metrics for the Varnika corpus of 3,533 multilingual idioms.

What carries the argument

Hybrid Mixture-of-Experts (HybridMoE) that integrates selected and unselected expert outputs via controlled hybridization plus Idiomatic Property Signals from masked multimodal embeddings.

If this is right

  • Literal translation fidelity, visual-semantic alignment, and idiomatic meaning retention all rise together when hybridization is applied.
  • The same signals and hybridization steps can be added to existing vision-language models without retraining the entire system.
  • Seven idiomatic tone categories become measurable and improvable in a single multimodal pipeline.
  • Performance lifts appear across Hindi, Bengali, and Thai, suggesting transfer to other low-resource language pairs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The hybridization step may generalize to non-idiomatic figurative devices such as metaphor or sarcasm if the property signals are redefined.
  • Because unselected experts still contribute, the method could reduce the number of active experts needed at inference time.
  • The three-stage pipeline could serve as a template for evaluating other culturally loaded phenomena like proverbs or humor.

Load-bearing premise

That controlled hybridization of selected and unselected experts plus masked idiomatic signals captures the seven tones without bias or overfitting.

What would settle it

A side-by-side run on the Varnika corpus and IDIO-TONE pipeline in which HybridMoE shows no gain or a loss relative to a standard MoE baseline on idiomatic meaning retention.

Figures

Figures reproduced from arXiv: 2606.01671 by Amaan Ali, Kitsuchart Pasupa, Sarmistha Das, Shreyas Guha, Sriparna Saha, Vaibhav Vishal.

Figure 1
Figure 1. Figure 1: Visual Representation of Idiomatic Understanding via Hybrid Mixture-of-Experts. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Sample instances of our proposed Varnika [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Architectural Viewpoint of Proposed Multimodal HybridMoE Model. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Expert Evaluation of Idiom Understanding Performance of VLM models with Idiomatic Property Reten [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparative Qualitative and Error Analyses [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of Idiomatic Tone categories across Hindi (h), Bengali (b), and Thai (t). [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Idiomatic Tonality label validations by VLM DeepSeek-R1 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
read the original abstract

In the contemporary epoch of multilingual education, learning idioms provides a fascinating gateway towards creativity, cultural values, historical context, and diverse perspectives inherent to various linguistic traditions. This paper showcases the navigation of retaining figurative and cultural semantics in low-resource Southeast Asian languages such as Hindi, Bengali, and Thai, where culturally rich idioms pose significant obstacles for computational modeling and cross-linguistic transfer due to their deep metaphorical complexity. To tackle such complexity, we present Varnika, a reconstructed multimodal idiom corpus comprising 3,533 multilingual idioms, enriched with seven idiomatic tones aligned with both textual and visual representations. Additionally, to infer informative idiomatic understanding, we introduce a Hybrid Mixture-of-Experts (HybridMoE) framework that embeds multiple idiomatic expert opinions while mitigating expert sparsity by integrating outputs from both selected and unselected experts through controlled hybridization, further augmented with Idiomatic Property Signals via masked multimodal embeddings. To analyze the performance across multiple dimensions, we propose the IDIO-TONE and Idiomatic Validation Score, a three-stage evaluation pipeline measuring (i) literal translation fidelity, (ii) visual-semantic alignment, and (iii) idiomatic meaning retention. Empirical evaluations highlight that HybridMoE achieves 5--6\% performance gains across advanced vision language models, demonstrating improved representation of figurative language and culturally embedded meaning in multilingual multimodal settings

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The manuscript introduces Varnika, a multimodal corpus of 3,533 idioms in Hindi, Bengali, and Thai annotated with seven idiomatic tones, and proposes a HybridMoE architecture that performs controlled hybridization of selected and unselected experts augmented by Idiomatic Property Signals from masked multimodal embeddings. It defines a new three-stage IDIO-TONE / Idiomatic Validation Score pipeline (literal translation fidelity, visual-semantic alignment, idiomatic meaning retention) and claims that HybridMoE yields 5–6 % gains over advanced vision-language models in multilingual figurative-language settings.

Significance. If the new metrics prove reliable and the experimental claims are reproducible, the work would address a genuine gap in culturally grounded multimodal idiom modeling for low-resource languages. At present, however, the absence of metric validation and experimental controls makes it impossible to determine whether the reported gains reflect improved idiomatic understanding or artifacts of the evaluation design.

major comments (3)
  1. [Abstract / Evaluation pipeline] Abstract / Evaluation section: IDIO-TONE and the Idiomatic Validation Score are introduced as the sole basis for the 5–6 % gain claim, yet the manuscript supplies no construction details, inter-annotator agreement statistics, correlation with human judgments, or comparison against existing idiom benchmarks. Without such grounding the numerical improvements cannot be interpreted as evidence of better figurative or cultural representation.
  2. [Abstract] Empirical results (abstract): The headline performance claim is presented without baselines, statistical significance tests, error bars, or data-exclusion criteria. This omission prevents verification that the observed gains are attributable to HybridMoE rather than experimental artifacts or metric-specific biases.
  3. [Abstract / HybridMoE framework] Framework description (abstract): The 'controlled hybridization' mechanism that integrates unselected experts is described only at a high level; no equations, hyper-parameter schedules, or ablation studies are supplied to demonstrate that the hybridization parameters are independent of the IDIO-TONE metrics. This leaves open the possibility of circularity between model design and evaluation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting areas where additional rigor is needed. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Abstract / Evaluation pipeline] Abstract / Evaluation section: IDIO-TONE and the Idiomatic Validation Score are introduced as the sole basis for the 5–6 % gain claim, yet the manuscript supplies no construction details, inter-annotator agreement statistics, correlation with human judgments, or comparison against existing idiom benchmarks. Without such grounding the numerical improvements cannot be interpreted as evidence of better figurative or cultural representation.

    Authors: We agree that the submitted manuscript lacks these grounding details for IDIO-TONE and the Idiomatic Validation Score. In the revision we will add a dedicated subsection with annotation construction process, inter-annotator agreement statistics, correlation analysis against human judgments, and direct comparisons to prior idiom benchmarks. This addresses the interpretability concern directly. revision: yes

  2. Referee: [Abstract] Empirical results (abstract): The headline performance claim is presented without baselines, statistical significance tests, error bars, or data-exclusion criteria. This omission prevents verification that the observed gains are attributable to HybridMoE rather than experimental artifacts or metric-specific biases.

    Authors: We agree the abstract and results presentation omit these elements. The revision will update the abstract to reference key baselines, include statistical significance tests, error bars, and explicit data-exclusion criteria, ensuring the 5–6% gains can be properly attributed and verified. revision: yes

  3. Referee: [Abstract / HybridMoE framework] Framework description (abstract): The 'controlled hybridization' mechanism that integrates unselected experts is described only at a high level; no equations, hyper-parameter schedules, or ablation studies are supplied to demonstrate that the hybridization parameters are independent of the IDIO-TONE metrics. This leaves open the possibility of circularity between model design and evaluation.

    Authors: We agree the abstract-level description is high-level and that equations, hyper-parameter schedules, and ablations are absent. The revision will supply the mathematical formulation of controlled hybridization, tuning schedules, and ablation studies on held-out data to demonstrate parameter independence from IDIO-TONE and eliminate circularity concerns. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation remains self-contained

full rationale

The provided abstract and description introduce a new corpus (Varnika), HybridMoE framework, and IDIO-TONE/Idiomatic Validation Score metrics via explicit three-stage pipeline definitions, then report empirical gains on those metrics. No equations, parameter-fitting steps, or derivations are shown that reduce the 5-6% gains or hybridization claims to the inputs by construction. No self-citation chains, ansatz smuggling, or renaming of known results appear in the text. The central claims are presented as independent empirical observations on the defined pipeline, satisfying the default expectation of non-circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient detail to identify specific free parameters, axioms, or invented entities; the HybridMoE hybridization parameters and tone definitions are presented as novel but their foundational assumptions are not elaborated.

pith-pipeline@v0.9.1-grok · 5796 in / 1222 out tokens · 36410 ms · 2026-06-28T15:15:28.717426+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

136 extracted references · 4 canonical work pages

  1. [1]

    Aho and Jeffrey D

    Alfred V. Aho and Jeffrey D. Ullman , title =. 1972

    1972

  2. [2]

    Publications Manual , year = "1983", publisher =

    1983

  3. [3]

    and Kozen, Dexter C

    Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer , year = "1981", title =. doi:10.1145/322234.322243

    work page doi:10.1145/322234.322243 1981

  4. [4]

    Scalable training of

    Andrew, Galen and Gao, Jianfeng , booktitle=. Scalable training of

  5. [5]

    Dan Gusfield , title =. 1997

    1997

  6. [6]

    Tetreault , title =

    Mohammad Sadegh Rasooli and Joel R. Tetreault , title =. Computing Research Repository , volume =. 2015 , url =

    2015

  7. [7]

    A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =

    Ando, Rie Kubota and Zhang, Tong , Issn =. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =. Journal of Machine Learning Research , Month = dec, Numpages =

  8. [8]

    2014 , publisher =

    Ekarat Udomporn , title =. 2014 , publisher =

    2014

  9. [9]

    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    Drishtikon: A multimodal multilingual benchmark for testing language models’ understanding on indian culture , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    2025

  10. [10]

    Findings of the Association for Computational Linguistics: ACL 2025 , pages=

    SANSKRITI: A comprehensive benchmark for evaluating language models’ knowledge of Indian culture , author=. Findings of the Association for Computational Linguistics: ACL 2025 , pages=

    2025

  11. [11]

    Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

    Clancey, William J. Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83)

  12. [12]

    , author=

    Why Teach Idioms? A Challenge to the Profession. , author=. Iranian Journal of Language Teaching Research , volume=. 2017 , publisher=

    2017

  13. [13]

    Classification Problem Solving

    Clancey, William J. Classification Problem Solving. Proceedings of the Fourth National Conference on Artificial Intelligence

  14. [14]

    , title =

    Robinson, Arthur L. , title =. 1980 , doi =. https://science.sciencemag.org/content/208/4447/1019.full.pdf , journal =

    1980

  15. [15]

    New Ways to Make Microcircuits Smaller---Duplicate Entry

    Robinson, Arthur L. New Ways to Make Microcircuits Smaller---Duplicate Entry. Science

  16. [16]

    Clancey and Glenn Rennels , abstract =

    Diane Warner Hasling and William J. Clancey and Glenn Rennels , abstract =. Strategic explanations for a diagnostic consultation system , journal =. 1984 , issn =. doi:https://doi.org/10.1016/S0020-7373(84)80003-6 , url =

    work page doi:10.1016/s0020-7373(84)80003-6 1984

  17. [17]

    and Rennels, Glenn R

    Hasling, Diane Warner and Clancey, William J. and Rennels, Glenn R. and Test, Thomas. Strategic Explanations in Consultation---Duplicate. The International Journal of Man-Machine Studies

  18. [18]

    Poligon: A System for Parallel Problem Solving

    Rice, James. Poligon: A System for Parallel Problem Solving

  19. [19]

    Transfer of Rule-Based Expertise through a Tutorial Dialogue

    Clancey, William J. Transfer of Rule-Based Expertise through a Tutorial Dialogue

  20. [20]

    The Engineering of Qualitative Models

    Clancey, William J. The Engineering of Qualitative Models

  21. [21]

    2017 , eprint=

    Attention Is All You Need , author=. 2017 , eprint=

    2017

  22. [22]

    Pluto: The 'Other' Red Planet

    NASA. Pluto: The 'Other' Red Planet

  23. [23]

    arXiv preprint arXiv:2412.03555 , year=

    Paligemma 2: A family of versatile vlms for transfer , author=. arXiv preprint arXiv:2412.03555 , year=

    Pith/arXiv arXiv

  24. [24]

    arXiv preprint arXiv:2504.05299 , year=

    Smolvlm: Redefining small and efficient multimodal models , author=. arXiv preprint arXiv:2504.05299 , year=

    Pith/arXiv arXiv

  25. [25]

    , title =

    Turing, Alan M. , title =. Mind , volume =

  26. [26]

    Nature , volume =

    Learning Representations by Back-Propagating Errors , author =. Nature , volume =

  27. [27]

    Proceedings of the 10th European Conference on Artificial Intelligence (ECAI) , pages =

    Planning as Satisfiability , author =. Proceedings of the 10th European Conference on Artificial Intelligence (ECAI) , pages =

  28. [28]

    Artificial Intelligence , volume =

    Collaborative Plans for Complex Group Action , author =. Artificial Intelligence , volume =

  29. [29]

    The Entropy Formula for the

    Grisha Perelman , howpublished =. The Entropy Formula for the

  30. [30]

    arXiv preprint arXiv:2410.10594 , year=

    Visrag: Vision-based retrieval-augmented generation on multi-modality documents , author=. arXiv preprint arXiv:2410.10594 , year=

    Pith/arXiv arXiv

  31. [31]

    arXiv preprint arXiv:2411.18203 , year=

    Critic-v: Vlm critics help catch vlm errors in multimodal reasoning , author=. arXiv preprint arXiv:2411.18203 , year=

    arXiv

  32. [32]

    FROM THEORY TO PRACTICE: MEMORY STRATEGIES FOR EFFECTIVE IDIOM LEARNING , author=

  33. [33]

    Causality , author =

  34. [34]

    Structure and Interpretation of Computer Programs

    Harold Abelson and Gerald Jay Sussman and Julie Sussman. Structure and Interpretation of Computer Programs. 1985

    1985

  35. [35]

    arXiv preprint arXiv:2405.10579 , year=

    A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models , author=. arXiv preprint arXiv:2405.10579 , year=

    arXiv

  36. [36]

    12th Language Resources and Evaluation Conference: LREC 2020 , pages=

    MAGPIE: A large corpus of potentially idiomatic expressions , author=. 12th Language Resources and Evaluation Conference: LREC 2020 , pages=. 2020 , organization=

    2020

  37. [37]

    Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009) , pages=

    Unsupervised recognition of literal and non-literal use of idiomatic expressions , author=. Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009) , pages=

    2009

  38. [38]

    Proceedings of the LREC Workshop Towards a Shared Task for Multiword Expressions (MWE 2008) , pages=

    The VNC-tokens dataset , author=. Proceedings of the LREC Workshop Towards a Shared Task for Multiword Expressions (MWE 2008) , pages=. 2008 , organization=

    2008

  39. [39]

    Proceedings of the joint workshop on automatic knowledge base construction and web-scale knowledge extraction (AKBC-WEKEX) , pages=

    Annotated gigaword , author=. Proceedings of the joint workshop on automatic knowledge base construction and web-scale knowledge extraction (AKBC-WEKEX) , pages=

  40. [40]

    , author=

    Idioms in Context: The IDIX Corpus. , author=. LREC , year=

  41. [41]

    Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) , pages=

    Semeval-2013 task 5: Evaluating phrasal semantics , author=. Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) , pages=

    2013

  42. [42]

    Visual Information Extraction with Lixto

    Robert Baumgartner and Georg Gottlob and Sergio Flesca. Visual Information Extraction with Lixto. Proceedings of the 27th International Conference on Very Large Databases. 2001

    2001

  43. [43]

    International Conference on Machine Learning , pages=

    Multi-task reinforcement learning with context-based representations , author=. International Conference on Machine Learning , pages=. 2021 , organization=

    2021

  44. [44]

    arXiv preprint arXiv:2404.12464 , year=

    Normad: A benchmark for measuring the cultural adaptability of large language models , author=. arXiv preprint arXiv:2404.12464 , year=

    arXiv

  45. [45]

    Sci , volume=

    Vector representations of idioms in conversational systems , author=. Sci , volume=. 2022 , publisher=

    2022

  46. [46]

    arXiv preprint arXiv:2104.06541 , year=

    From solving a problem boldly to cutting the gordian knot: Idiomatic text generation , author=. arXiv preprint arXiv:2104.06541 , year=

    arXiv

  47. [47]

    arXiv preprint arXiv:2112.02994 , year=

    IBERT: Idiom Cloze-style reading comprehension with Attention , author=. arXiv preprint arXiv:2112.02994 , year=

    arXiv

  48. [48]

    arXiv preprint arXiv:1906.05317 , year=

    COMET: Commonsense transformers for automatic knowledge graph construction , author=. arXiv preprint arXiv:1906.05317 , year=

    Pith/arXiv arXiv 1906

  49. [49]

    Brachman and James G

    Ronald J. Brachman and James G. Schmolze. An overview of the KL-ONE knowledge representation system. Cognitive Science. 1985

    1985

  50. [50]

    Complexity results for nonmonotonic logics

    Georg Gottlob. Complexity results for nonmonotonic logics. Journal of Logic and Computation. 1992

    1992

  51. [51]

    2025 , howpublished =

    Hugging Face , title =. 2025 , howpublished =

    2025

  52. [52]

    arXiv preprint arXiv:2310.06825 , year=

    Mistral 7B , author=. arXiv preprint arXiv:2310.06825 , year=

    Pith/arXiv arXiv

  53. [53]

    arXiv preprint arXiv:1907.11692 , year=

    RoBERTa: A Robustly Optimized BERT Pretraining Approach , author=. arXiv preprint arXiv:1907.11692 , year=

    Pith/arXiv arXiv 1907

  54. [54]

    arXiv preprint arXiv:1910.01108 , year=

    DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter , author=. arXiv preprint arXiv:1910.01108 , year=

    Pith/arXiv arXiv 1910

  55. [55]

    arXiv preprint arXiv:1911.02116 , year=

    Unsupervised Cross-lingual Representation Learning at Scale , author=. arXiv preprint arXiv:1911.02116 , year=

    Pith/arXiv arXiv 1911

  56. [56]

    arXiv preprint arXiv:1810.04805 , year=

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , author=. arXiv preprint arXiv:1810.04805 , year=

    Pith/arXiv arXiv

  57. [57]

    Journal of Machine Learning Research , volume=

    Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , author=. Journal of Machine Learning Research , volume=

  58. [58]

    arXiv preprint arXiv:2407.21783 , year=

    The Llama 3 Herd of Models , author=. arXiv preprint arXiv:2407.21783 , year=

    Pith/arXiv arXiv

  59. [59]

    Advances in Neural Information Processing Systems , volume=

    Language Models are Few-Shot Learners , author=. Advances in Neural Information Processing Systems , volume=

  60. [60]

    Hypertree Decompositions and Tractable Queries

    Georg Gottlob and Nicola Leone and Francesco Scarcello. Hypertree Decompositions and Tractable Queries. Journal of Computer and System Sciences. 2002

    2002

  61. [61]

    Levesque

    Hector J. Levesque. Foundations of a functional approach to knowledge representation. Artificial Intelligence. 1984

    1984

  62. [62]

    arXiv preprint arXiv:2310.01852 , year=

    LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment , author=. arXiv preprint arXiv:2310.01852 , year=

    Pith/arXiv arXiv

  63. [63]

    arXiv preprint arXiv:2306.02858 , year =

    Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding , author =. arXiv preprint arXiv:2306.02858 , year =

    Pith/arXiv arXiv

  64. [64]

    arXiv preprint arXiv:2409.12191 , year=

    Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution , author=. arXiv preprint arXiv:2409.12191 , year=

    Pith/arXiv arXiv

  65. [65]

    Junnan Li and Dongxu Li and Silvio Savarese and Steven Hoi , year=

  66. [66]

    2022 , booktitle=

    BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation , author=. 2022 , booktitle=

    2022

  67. [67]

    Levesque

    Hector J. Levesque. A logic of implicit and explicit belief. Proceedings of the Fourth National Conference on Artificial Intelligence. 1984

    1984

  68. [68]

    arXiv preprint arXiv:2405.17247 , year=

    An introduction to vision-language modeling , author=. arXiv preprint arXiv:2405.17247 , year=

    arXiv

  69. [69]

    Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence , pages=

    Integrating LLM, VLM, and Text-to-Image Models for Enhanced Information Graphics: A Methodology for Accurate and Visually Engaging Visualizations , author=. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence , pages=

  70. [70]

    Advances in Neural Information Processing Systems , volume=

    Visogender: A dataset for benchmarking gender bias in image-text pronoun resolution , author=. Advances in Neural Information Processing Systems , volume=

  71. [71]

    arXiv preprint arXiv:2406.11069 , year=

    WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences , author=. arXiv preprint arXiv:2406.11069 , year=

    arXiv

  72. [72]

    Frontiers in Artificial Intelligence , volume=

    Vision-language models for medical report generation and visual question answering: A review , author=. Frontiers in Artificial Intelligence , volume=. 2024 , publisher=

    2024

  73. [73]

    On the compilability and expressive power of propositional planning formalisms

    Bernhard Nebel. On the compilability and expressive power of propositional planning formalisms. Journal of Artificial Intelligence Research. 2000

    2000

  74. [74]

    arXiv preprint arXiv:1909.11942 , year=

    Albert: A lite bert for self-supervised learning of language representations , author=. arXiv preprint arXiv:1909.11942 , year=

    Pith/arXiv arXiv 1909

  75. [75]

    Advances in neural information processing systems , volume=

    Training language models to follow instructions with human feedback , author=. Advances in neural information processing systems , volume=

  76. [76]

    Journal of Experimental Social Psychology , volume=

    When what you say about others says something about you: Language abstraction and inferences about describers’ attitudes and goals , author=. Journal of Experimental Social Psychology , volume=. 2006 , publisher=

    2006

  77. [77]

    Anthropological quarterly , pages=

    Proverbs: Metaphors that teach , author=. Anthropological quarterly , pages=. 1988 , publisher=

    1988

  78. [78]

    Western Folklore , volume=

    Tensions in proverbs: more light on international understanding , author=. Western Folklore , volume=. 1956 , publisher=

    1956

  79. [79]

    , author=

    Proverbs and cultural models: An American psychology of problem solving. , author=. 1987 , publisher=

    1987

  80. [80]

    The Journal of Experimental Education , volume=

    Measuring relational reasoning , author=. The Journal of Experimental Education , volume=. 2016 , publisher=

    2016

Showing first 80 references.