pith. sign in

arxiv: 2606.02147 · v1 · pith:SPROYAJ3new · submitted 2026-06-01 · 💻 cs.CL · cs.AI

Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages

Saeed Almheiri , Bilal Elbouardi , Salsabila Zahirah Pranida , Irina Nikishina , Ashwath Rao B , Parameswari Krishnamurthy , Muhammad Cendekia Airlangga , Rifo Ahmad Genadi
show 11 more authors
Nguyen Phan Gia Bao Amir Hossein Yari Hawau Olamide Toyin Nurdaulet Mukhituly Mena Attia Besher Hassan Ahmad Fathan Hidayatullah Tatsuki Kuribayashi Haonan Li Suma Bhat Fajri Koto
This is my paper

Pith reviewed 2026-06-28 14:35 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords multilingual idiomsMIDI datasetlow-resource languagesliteral figurative interpretationsconversational contextNLP model benchmarkingidiom comprehension
0
0 comments X

The pith

State-of-the-art models understand idioms less accurately in low-resource languages, and literal readings are harder than figurative ones in every language tier tested.

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

This paper introduces the MIDI dataset, which contains idioms placed inside both single sentences and full conversations across eighteen languages spanning high, medium, and low resource levels, with each idiom labeled for both its literal and figurative meanings by native speakers. Benchmarking experiments on current models demonstrate that performance declines as the amount of available training data for a language decreases and that literal senses consistently lag behind figurative ones regardless of resource level. Adding the surrounding conversational context raises accuracy for all languages yet fails to remove either the resource-based or the literal-figurative gaps. The work also includes tests that distinguish memorization of training examples from genuine reasoning about the provided context. These results matter because they show where existing multilingual systems still fall short when faced with the non-literal language that occurs in ordinary human communication.

Core claim

The central discovery is that idiom comprehension in multilingual models degrades in low-resource languages, literal interpretations are substantially harder than figurative ones across all tiers, and conversational context improves results without eliminating the disparities, as shown through benchmarking on the MIDI dataset of native-curated examples in sentence and conversational settings.

What carries the argument

The MIDI dataset, which embeds idioms in sentence-level and conversational contexts and supplies both literal and figurative readings for high-, medium-, and low-resource languages.

If this is right

  • Idiom comprehension accuracy decreases from high- to low-resource languages.
  • Literal interpretations remain substantially harder than figurative ones in all resource tiers.
  • Conversational context improves performance but does not close the gaps between resource levels or between literal and figurative readings.
  • Controlled interventions on model representations can distinguish memorization from reasoning about idioms.

Where Pith is reading between the lines

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

  • Low-resource languages may require dedicated idiom-focused data collection or augmentation strategies during pretraining.
  • The observed gaps point to the need for model components that explicitly model non-compositional meaning shifts.
  • Similar patterns might appear in other non-compositional phenomena such as metaphors or sarcasm across resource levels.

Load-bearing premise

Native speakers can reliably curate and label idioms with accurate literal and figurative readings that are consistent and representative across the selected languages, contexts, and resource tiers.

What would settle it

A finding that model accuracy on MIDI examples is independent of language resource level, or that literal and figurative accuracy are equal, would falsify the reported disparities.

Figures

Figures reproduced from arXiv: 2606.02147 by Ahmad Fathan Hidayatullah, Amir Hossein Yari, Ashwath Rao B, Besher Hassan, Bilal Elbouardi, Fajri Koto, Haonan Li, Hawau Olamide Toyin, Irina Nikishina, Mena Attia, Muhammad Cendekia Airlangga, Nguyen Phan Gia Bao, Nurdaulet Mukhituly, Parameswari Krishnamurthy, Rifo Ahmad Genadi, Saeed Almheiri, Salsabila Zahirah Pranida, Suma Bhat, Tatsuki Kuribayashi.

Figure 1
Figure 1. Figure 1: We compile idioms and their sentence￾and dialogue-level usages from 18 languages spanning high-, medium-, and low-resource contexts, then eval￾uate LLMs with multiple-choice and binary inference tasks targeting both figurative vs. literal understanding and biased interpretations. correctly inferring them in context requires inte￾grating nuanced cultural cues and reasoning-based inference (Cacciari and Tabo… view at source ↗
Figure 2
Figure 2. Figure 2: MIDI construction pipeline. Native speakers collect and annotate idioms (18 languages) with bilingual definitions, then create figurative/literal sentence contexts and LLM-generated (manually revised) dialogues with MCQ options, producing paired figurative and literal usage subsets. 2 Related Work A significant body of literature has introduced var￾ious idiom datasets, reflecting a growing interest in the … view at source ↗
Figure 3
Figure 3. Figure 3: Interpretation bias (∆ = figurative–literal preference, %) vs. idiom comprehension accuracy: models sorted by (a) figurative and (b) literal accuracy (parentheses show accuracies %) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Prompt used to generate dialogues where the [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Interpretation bias (∆) sorted by figurative accuracy. Positive ∆ indicates figurative preference. Scores in parentheses denote weighted accuracy. tion (not used in our primary experiments): h (ℓ) (x) ← h (ℓ) (x) − rˆ (ℓ)  rˆ (ℓ)⊤h (ℓ) (x)  . (4) H.2 Where Steering Helps Figures 8 and 9 report steering-induced accuracy changes (in percentage points), stratified by re￾source tier. A consistent pattern eme… view at source ↗
Figure 8
Figure 8. Figure 8: Steering-induced accuracy gains (∆) using MMLU-Pro vectors, broken down by task and resource tier. I Human Evaluation Details To verify that MIDI’s idiom comprehension task setup (Section 4.2; see format D.1) is clear and interpretable for human annotators, and to estab￾lish a reference point against which model perfor￾mance can be contextualized, we conducted a hu￾man evaluation on a 10% random sample of … view at source ↗
Figure 9
Figure 9. Figure 9: Steering-induced accuracy gains (∆) using MIDI-derived vectors, broken down by task and resource tier. overall open-source model, Gemma-3 (27B), on the exact same 10% subset that was labeled by human annotators. Tables 19 and 20 presents the resulting sample-level scores, broken down by context and usage type. On this subset, Gemini 2.5 Pro aver￾ages 80% overall, while Gemma-3 averages 78%, both well below… view at source ↗
Figure 10
Figure 10. Figure 10: ℓ2 norms of the memorization→reasoning direction vector across swept layers (log scale). model), highlighting considerable remaining room for improvement in both high- and low-resource tiers, and in particular for open-source models on low-resource languages. The full dataset comparison in [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of best-performing layers (selected by highest development accuracy) from the layer sweep. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Flip rates on the full evaluation set, macro-averaged across languages within each resource tier and [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
read the original abstract

Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. Prior work has focused on high-resource languages typically evaluates isolated idiom-meaning questions, overlooking realistic discourse. We introduce MIDI, a multilingual idiom dataset spanning 3 high-, 3 medium-, and 12 low-resource languages, curated by native speakers. Unlike previous datasets, MIDI provides idioms embedded in both sentence-level and conversational contexts, capturing both literal and figurative readings. Benchmarking state-of-the-art models shows that idiom comprehension degrades in low-resource languages and that, in all resource tiers, literal interpretations are substantially harder than figurative ones. Conversational context improves performance but does not eliminate these disparities. Through controlled tests and interventions on hidden representations, we further separate memorization from reasoning, exposing core limitations of current models.

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

1 major / 2 minor

Summary. The manuscript introduces MIDI, a multilingual idiom dataset spanning 3 high-, 3 medium-, and 12 low-resource languages, with idioms embedded in both sentence-level and conversational contexts and supplied with literal and figurative readings by native speakers. Benchmarking of state-of-the-art models shows performance degradation in low-resource languages, substantially greater difficulty with literal than figurative interpretations across all tiers, and partial improvement from conversational context that does not eliminate the gaps. Controlled interventions on hidden representations are used to separate memorization from reasoning.

Significance. If the labels prove reliable, MIDI would be a valuable new resource for evaluating idiom comprehension in realistic discourse across resource tiers, extending prior work that has focused mainly on high-resource languages and isolated sentences. The use of interventions to distinguish memorization from reasoning is a methodological strength that could help diagnose model limitations.

major comments (1)
  1. [Dataset construction section] Dataset construction section: No inter-annotator agreement metrics, held-out validation set, or cross-validation against existing idiom lexicons are reported for the native-speaker curated literal/figurative labels and contexts. This is load-bearing for the central claims, as higher label noise in the 12 low-resource languages (where inventories are smaller) could artifactually produce the reported resource-tier gaps and literal-vs-figurative disparity.
minor comments (2)
  1. [Abstract and benchmarking results section] Abstract and benchmarking results section: Explicit dataset sizes per language/tier, model specifications, exact evaluation metrics, and any statistical tests should be stated to support the performance claims.
  2. [Figures and tables] Figure and table captions: Some captions lack sufficient detail on what is being plotted (e.g., exact accuracy definitions or context conditions).

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the importance of validating the MIDI labels. We address this point directly below and will strengthen the dataset construction section accordingly.

read point-by-point responses

  1. Referee: [Dataset construction section] Dataset construction section: No inter-annotator agreement metrics, held-out validation set, or cross-validation against existing idiom lexicons are reported for the native-speaker curated literal/figurative labels and contexts. This is load-bearing for the central claims, as higher label noise in the 12 low-resource languages (where inventories are smaller) could artifactually produce the reported resource-tier gaps and literal-vs-figurative disparity.

    Authors: We agree that explicit validation metrics would increase confidence in the labels. In the revision we will add inter-annotator agreement figures computed on the subset of items that received multiple independent native-speaker annotations, together with a description of the annotation protocol. A held-out validation subset can also be designated and reported. Cross-validation against external lexicons is feasible only for the high- and medium-resource languages; for the twelve low-resource languages no comparable public resources with literal/figurative distinctions exist, and we will state this limitation explicitly. We note, however, that the literal-versus-figurative performance gap is observed uniformly across all three resource tiers, including the high-resource languages where label quality is least likely to be an issue. This pattern is difficult to attribute solely to differential noise in the low-resource portion of the data. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical dataset creation and benchmarking

full rationale

The paper introduces the MIDI dataset (curated by native speakers) and reports empirical model evaluations on sentence vs. conversational contexts, literal vs. figurative readings, and resource tiers. No equations, fitted parameters, derivations, or predictions appear in the abstract or described content. No self-citations are invoked to justify core claims. The results are direct measurements on the constructed data and do not reduce to any input by construction. This is a standard empirical NLP study with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on domain assumptions about reliable native-speaker labeling of literal versus figurative idiom uses and the representativeness of the chosen languages for their resource categories.

axioms (1)
  • domain assumption Idiomatic expressions have distinguishable literal and figurative meanings that native speakers can consistently identify and label.
    This assumption enables the dataset construction and the literal/figurative performance comparisons.

pith-pipeline@v0.9.1-grok · 5781 in / 1121 out tokens · 34097 ms · 2026-06-28T14:35:35.108116+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

32 extracted references · 14 canonical work pages

  1. [1]

    Advances in Neural Information Processing Systems , volume=

    Mmlu-pro: A more robust and challenging multi-task language understanding benchmark , author=. Advances in Neural Information Processing Systems , volume=

  2. [2]

    The State and Fate of Linguistic Diversity and Inclusion in the NLP World

    Joshi, Pratik and Santy, Sebastin and Budhiraja, Amar and Bali, Kalika and Choudhury, Monojit. The State and Fate of Linguistic Diversity and Inclusion in the NLP World. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.560

    work page doi:10.18653/v1/2020.acl-main.560 2020

  3. [3]

    arXiv preprint arXiv:2207.04672 , year=

    No language left behind: Scaling human-centered machine translation , author=. arXiv preprint arXiv:2207.04672 , year=

    Pith/arXiv arXiv

  4. [4]

    Non-compositional Expression Generation and its Continual Learning

    Zhou, Jianing and Bhat, Suma. Non-compositional Expression Generation and its Continual Learning. Findings of the Association for Computational Linguistics: ACL 2024. 2024. doi:10.18653/v1/2024.findings-acl.166

    work page doi:10.18653/v1/2024.findings-acl.166 2024

  5. [5]

    First Conference on Language Modeling , year=

    Enhancing Language Models with Idiomatic Reasoning , author=. First Conference on Language Modeling , year=

  6. [6]

    , author=

    Multiword expressions. , author=. Handbook of natural language processing , volume=

  7. [7]

    Acta Universitatis Sapientiae, Philologica , volume=

    About the definition, classification, and translation strategies of idioms , author=. Acta Universitatis Sapientiae, Philologica , volume=. 2016 , publisher=

    2016

  8. [8]

    Journal of memory and language , volume=

    The comprehension of idioms , author=. Journal of memory and language , volume=. 1988 , publisher=

    1988

  9. [9]

    Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation

    Dankers, Verna and Lucas, Christopher and Titov, Ivan. Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022. doi:10.18653/v1/2022.acl-long.252

    work page doi:10.18653/v1/2022.acl-long.252 2022

  10. [10]

    Rolling the DICE on Idiomaticity: How LLM s Fail to Grasp Context

    Mi, Maggie and Villavicencio, Aline and Moosavi, Nafise Sadat. Rolling the DICE on Idiomaticity: How LLM s Fail to Grasp Context. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. doi:10.18653/v1/2025.acl-long.362

    work page doi:10.18653/v1/2025.acl-long.362 2025

  11. [11]

    ID 10 M : Idiom Identification in 10 Languages

    Tedeschi, Simone and Martelli, Federico and Navigli, Roberto. ID 10 M : Idiom Identification in 10 Languages. Findings of the Association for Computational Linguistics: NAACL 2022. 2022. doi:10.18653/v1/2022.findings-naacl.208

    work page doi:10.18653/v1/2022.findings-naacl.208 2022

  12. [12]

    A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models

    De Luca Fornaciari, Francesca and Altuna, Bego \ n a and Gonzalez-Dios, Itziar and Melero, Maite. A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models. Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024). 2024. doi:10.18653/v1/2024.figlang-1.5

    work page doi:10.18653/v1/2024.figlang-1.5 2024

  13. [13]

    Comparative Study of Multilingual Idioms and Similes in Large Language Models

    Khoshtab, Paria and Namazifard, Danial and Masoudi, Mostafa and Akhgary, Ali and Mahdizadeh Sani, Samin and Yaghoobzadeh, Yadollah. Comparative Study of Multilingual Idioms and Similes in Large Language Models. Proceedings of the 31st International Conference on Computational Linguistics. 2025

    2025

  14. [14]

    Transactions of the Association for Computational Linguistics , volume =

    Zeng, Ziheng and Bhat, Suma , title =. Transactions of the Association for Computational Linguistics , volume =. 2021 , month =. doi:10.1162/tacl_a_00442 , url =

    work page doi:10.1162/tacl_a_00442 2021

  15. [15]

    Multilingual Multi-Figurative Language Detection

    Lai, Huiyuan and Toral, Antonio and Nissim, Malvina. Multilingual Multi-Figurative Language Detection. Findings of the Association for Computational Linguistics: ACL 2023. 2023. doi:10.18653/v1/2023.findings-acl.589

    work page doi:10.18653/v1/2023.findings-acl.589 2023

  16. [16]

    and Tanner, Darren , title =

    Bulkes, Nyssa Z. and Tanner, Darren , title =. Behavior Research Methods , volume =. 2017 , doi =

    2017

  17. [17]

    Are Multilingual LLM s Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings

    Cecilia Liu, Chen and Koto, Fajri and Baldwin, Timothy and Gurevych, Iryna. Are Multilingual LLM s Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 20...

    work page doi:10.18653/v1/2024.naacl-long.112 2024

  18. [18]

    and Titone, Debra A

    Libben, Maya R. and Titone, Debra A. , title =. Memory & Cognition , year =. doi:10.3758/MC.36.6.1103 , url =

    work page doi:10.3758/mc.36.6.1103

  19. [19]

    Memorization or Reasoning? Exploring the Idiom Understanding of LLM s

    Kim, Jisu and Shin, Youngwoo and Hwang, Uiji and Choi, Jihun and Xuan, Richeng and Kim, Taeuk. Memorization or Reasoning? Exploring the Idiom Understanding of LLM s. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.1099

    work page doi:10.18653/v1/2025.emnlp-main.1099 2025

  20. [20]

    LI dioms: A Multilingual Linked Idioms Data Set

    Moussallem, Diego and Sherif, Mohamed Ahmed and Esteves, Diego and Zampieri, Marcos and Ngonga Ngomo, Axel-Cyrille. LI dioms: A Multilingual Linked Idioms Data Set. Proceedings of the Eleventh International Conference on Language Resources and Evaluation ( LREC 2018). 2018

    2018

  21. [21]

    MAGPIE : A Large Corpus of Potentially Idiomatic Expressions

    Haagsma, Hessel and Bos, Johan and Nissim, Malvina. MAGPIE : A Large Corpus of Potentially Idiomatic Expressions. Proceedings of the Twelfth Language Resources and Evaluation Conference. 2020

    2020

  22. [22]

    AS titch I n L anguage M odels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models

    Tayyar Madabushi, Harish and Gow-Smith, Edward and Scarton, Carolina and Villavicencio, Aline. AS titch I n L anguage M odels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models. Findings of the Association for Computational Linguistics: EMNLP 2021. 2021. doi:10.18653/v1/2021.findings-emnlp.294

    work page doi:10.18653/v1/2021.findings-emnlp.294 2021

  23. [23]

    2023 , eprint =

    Steering Language Models With Activation Engineering , author =. 2023 , eprint =

    2023

  24. [24]

    The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Direction

    Hong, Yihuai and Cao, Meng and Zhou, Dian and Yu, Lei and Jin, Zhijing. The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Direction. Findings of the Association for Computational Linguistics: ACL 2025. 2025. doi:10.18653/v1/2025.findings-acl.1111

    work page doi:10.18653/v1/2025.findings-acl.1111 2025

  25. [25]

    2025 , eprint=

    Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities , author=. 2025 , eprint=

    2025

  26. [26]

    2025 , eprint=

    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning , author=. 2025 , eprint=

    2025

  27. [27]

    2025 , eprint=

    Gemma 3 Technical Report , author=. 2025 , eprint=

    2025

  28. [28]

    2025 , eprint=

    Qwen3 Technical Report , author=. 2025 , eprint=

    2025

  29. [29]

    2024 , eprint=

    Mixtral of Experts , author=. 2024 , eprint=

    2024

  30. [30]

    2024 , eprint=

    The Llama 3 Herd of Models , author=. 2024 , eprint=

    2024

  31. [31]

    2025 , url=

    GPT-5 System Card , author=. 2025 , url=

    2025

  32. [32]

    doi:10.5281/zenodo.12608602 , url =

    Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang...

    work page doi:10.5281/zenodo.12608602