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arxiv: 2605.28782 · v1 · pith:EXBJCCHRnew · submitted 2026-05-27 · 💻 cs.CL

Can Large Language Models Handle Discourse Particles? A Case Study of Colloquial Malay

Mariah Al Giptiah Binte Yusoff , Jakin Tan , Bocheng Chen , Guangliang Liu , Xi Chen This is my paper

Pith reviewed 2026-06-29 13:15 UTC · model grok-4.3

classification 💻 cs.CL
keywords discourse particlescolloquial Malaypragmatic functionslarge language modelsbenchmarkLLM evaluationSoutheast Asian languages
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The pith

LLMs struggle to connect discourse particles in colloquial Malay to their pragmatic functions, but five linguistic attributes improve performance.

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

Discourse particles in Malay convey speaker intent, emotion, and interpersonal meaning beyond literal words. The paper builds MalayPrag, a benchmark of three prediction tasks, and defines five attributes that give a unified description of each particle's pragmatic role. Ten off-the-shelf LLMs are tested and show clear difficulty linking particles to functions. Supplying the five attributes in prompts produces significant gains on the tasks. The work therefore argues that explicit structured scaffolding is needed for models to acquire pragmatic competence in this language.

Core claim

The paper establishes that current large language models face substantial challenges in accurately associating discourse particles with their pragmatic functions in colloquial Malay, yet the addition of five linguistically grounded attributes leads to marked improvements across the three prediction tasks, demonstrating that structured linguistic information can scaffold pragmatic understanding.

What carries the argument

The MalayPrag benchmark with its three prediction tasks together with the framework of five attributes that interpret pragmatic functions of discourse particles.

If this is right

  • LLMs require explicit linguistic scaffolding to handle pragmatic features such as discourse particles reliably.
  • Attribute-based prompting can raise accuracy on pragmatic prediction tasks in colloquial Malay.
  • Benchmarks focused on Southeast Asian languages expose capability gaps that English-centric tests miss.
  • Pragmatic competence benefits from structured external frameworks rather than emerging automatically from model scale alone.

Where Pith is reading between the lines

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

  • The same attribute scaffolding approach may help LLMs manage discourse particles in other languages that rely heavily on them.
  • Training pipelines could incorporate attribute-style descriptions to strengthen multilingual pragmatic handling.
  • Underrepresentation of colloquial pragmatic markers from Southeast Asian languages in pretraining data may explain baseline weaknesses.

Load-bearing premise

The MalayPrag tasks and the five attributes together provide a valid, unconfounded measure of LLM pragmatic competence for discourse particles in colloquial Malay.

What would settle it

An experiment in which the same models show no performance gain or reduced accuracy when the five attributes are added to the prompts.

Figures

Figures reproduced from arXiv: 2605.28782 by Bocheng Chen, Guangliang Liu, Jakin Tan, Mariah Al Giptiah Binte Yusoff, Xi Chen.

Figure 1
Figure 1. Figure 1: Five-dimensional annotation schema for Malay discourse particle utterances. An utterance is evaluated by [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of Gold and Silver annotation [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
read the original abstract

Discourse particles, such as \textit{well} and \textit{kind of}, are crucial components that enable LLMs to ``speak'' more like humans. They are used to convey emotions, intentions, and interpersonal meanings. However, existing studies have not yet built a comprehensive understanding of LLMs' capabilities in handling discourse particles. Moreover, the limited number of studies focuses primarily on high-resource languages such as English, with little attention paid to Southeast Asian languages. In this paper, we (1) propose \textsc{MalayPrag}, a benchmark designed to systematically evaluate and analyze LLMs' capabilities in handling discourse particles in colloquial Malay; and (2) introduce five attributes that provide a linguistically grounded, unified framework for interpreting the pragmatic functions of discourse particles. Applying these two contributions, we prompt ten off-the-shelf LLMs to perform three prediction tasks. The experimental results reveal substantial challenges for current LLMs in accurately connecting discourse particles with their pragmatic functions in Malay. The provision of the five attributes designed in this study is found to significantly improve these connections, highlighting the need for structured scaffolding for models' pragmatic competence.

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 / 2 minor

Summary. The paper proposes the MalayPrag benchmark to evaluate LLMs on discourse particles in colloquial Malay and introduces five linguistically grounded attributes for pragmatic functions. It evaluates ten off-the-shelf LLMs on three prediction tasks, reporting substantial baseline challenges in linking particles to functions but significant gains when the attributes are supplied, and concludes that structured scaffolding is needed for pragmatic competence in this setting.

Significance. If the benchmark tasks and attributes form a valid, unconfounded measure of pragmatic competence, the work would usefully extend LLM evaluation to an under-studied low-resource language and pragmatic phenomenon. The creation of a new benchmark for colloquial Malay and the explicit attribute framework are concrete contributions that could template similar efforts for other Southeast Asian languages.

major comments (3)
  1. [§3] §3 (MalayPrag benchmark construction): the paper must detail the data collection, annotation protocol, inter-annotator agreement, and steps taken to ensure that the three prediction tasks cannot be solved by surface cues or lexical overlap rather than pragmatic inference; without this, baseline failures cannot be attributed specifically to pragmatic deficits as claimed in the abstract and §6.
  2. [§5] §5 (provision of the five attributes): the experimental design must clarify exactly how the attributes are inserted into the prompts for the 'with attributes' condition (e.g., as additional context versus explicit function labels). If the attributes function as direct labels for the target functions, the reported improvement does not demonstrate the value of 'structured scaffolding' but rather the effect of supplying the answer; this distinction is load-bearing for the central claim.
  3. [§4, §6] §4 and §6 (task definitions and results): the three prediction tasks and reported improvements lack any analysis of prompt sensitivity, potential training-data overlap with colloquial Malay, or coverage gaps in particle functions; these confounds directly affect whether the gains can be interpreted as evidence for improved pragmatic competence.
minor comments (2)
  1. [Abstract, §1] The abstract and §1 should explicitly state the size of the MalayPrag dataset, the exact number of particles covered, and the statistical tests used for significance of the reported improvements.
  2. [Tables/Figures] Table and figure captions should include the exact prompt templates and attribute definitions so that the experiments are reproducible from the text alone.

Simulated Author's Rebuttal

3 responses · 1 unresolved

Thank you for your thorough review and constructive feedback. We address each major comment point by point below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (MalayPrag benchmark construction): the paper must detail the data collection, annotation protocol, inter-annotator agreement, and steps taken to ensure that the three prediction tasks cannot be solved by surface cues or lexical overlap rather than pragmatic inference; without this, baseline failures cannot be attributed specifically to pragmatic deficits as claimed in the abstract and §6.

    Authors: We agree that expanded details on benchmark construction are required. In the revised §3 we will add: data sources (transcribed colloquial conversations and social media posts in Malay), annotation protocol (two native-speaker linguists independently labeling pragmatic functions), inter-annotator agreement (Cohen’s κ reported), and controls against surface cues (balanced lexical contexts, adversarial examples with particle-free variants, and explicit checks that lexical overlap does not predict labels). These additions will support the attribution of baseline failures to pragmatic inference. revision: yes

  2. Referee: [§5] §5 (provision of the five attributes): the experimental design must clarify exactly how the attributes are inserted into the prompts for the 'with attributes' condition (e.g., as additional context versus explicit function labels). If the attributes function as direct labels for the target functions, the reported improvement does not demonstrate the value of 'structured scaffolding' but rather the effect of supplying the answer; this distinction is load-bearing for the central claim.

    Authors: The attributes are supplied as general linguistic descriptors (e.g., “may signal softening or hedging”) rather than instance-specific function labels. The model must still map the descriptor to the particle’s use in the given sentence. We will include the exact prompt templates and an ablation showing that attribute descriptions alone do not reveal the target label. This clarifies that the gains reflect scaffolding rather than answer leakage. revision: yes

  3. Referee: [§4, §6] §4 and §6 (task definitions and results): the three prediction tasks and reported improvements lack any analysis of prompt sensitivity, potential training-data overlap with colloquial Malay, or coverage gaps in particle functions; these confounds directly affect whether the gains can be interpreted as evidence for improved pragmatic competence.

    Authors: We will add prompt-sensitivity experiments (multiple phrasings and few-shot variants) and a coverage analysis of particle functions in the benchmark. Direct inspection of training-data overlap is not possible for the closed-source models evaluated; we will therefore discuss this as a limitation while noting the low-resource status of colloquial Malay reduces the likelihood of substantial leakage. These changes qualify the interpretation of the results. revision: partial

standing simulated objections not resolved
  • Direct verification of training-data overlap for the closed-source LLMs evaluated.

Circularity Check

0 steps flagged

No circularity: empirical benchmark study with independent evaluation tasks

full rationale

The paper introduces MalayPrag benchmark and five attributes, then reports LLM performance on three prediction tasks via prompting experiments. No equations, fitted parameters renamed as predictions, self-citations used as load-bearing uniqueness theorems, or ansatzes smuggled in. The central claims rest on experimental outcomes rather than reducing to definitions or prior self-referential results by construction. This is a standard empirical evaluation paper whose validity hinges on external falsifiability of the benchmark tasks, not internal circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

The central claim rests on the validity of two newly invented evaluation constructs (the benchmark and the attribute set) that have no independent evidence or prior validation mentioned; no free parameters or mathematical axioms are involved as this is an empirical prompting study.

invented entities (2)
  • MalayPrag benchmark no independent evidence
    purpose: Systematic evaluation of LLMs on discourse particles in colloquial Malay
    Newly proposed in the paper with no prior existence or external validation referenced.
  • Five attributes for pragmatic functions no independent evidence
    purpose: Linguistically grounded unified framework to interpret discourse particle functions and provide scaffolding
    Designed specifically for this study with no independent evidence of completeness or prior use.

pith-pipeline@v0.9.1-grok · 5744 in / 1260 out tokens · 51834 ms · 2026-06-29T13:15:11.411133+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

4 extracted references · 1 canonical work pages

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    Laura Eline Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, and Edward Grefenstette

    Cross-genre argument mining: Can language models automatically fill in missing discourse mark- ers?Argument & Computation, 16(1):3–35. Laura Eline Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, and Edward Grefenstette

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    Large language models are not zero-shot com- municators. Emily Sadlier-Brown, Millie Lou, Miikka Silfverberg, and Carla Kam. 2024. How useful is context, ac- tually? comparing llms and humans on discourse marker prediction. InProceedings of the Workshop on Cognitive Modeling and Computational Linguis- tics, pages 231–241, Bangkok, Thailand. Association fo...

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    Yes” or “No

    Is it just semantics? a case study of discourse particle understanding in llms. InFindings of the As- sociation for Computational Linguistics: ACL 2025, pages 21704–21715, Vienna, Austria. Association for Computational Linguistics. Settaluri Lakshmi Sravanthi, Meet Doshi, Tankala Pa- van Kalyan, Pushpak Bhattacharyya, Rudra Murthy, and Raj Dabre. 2024. Pu...

    work page arXiv 2025

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    ke", "kan

    What is the speaker’s stance toward the listener (shared knowledge, seeking confirmation, neutral information, etc.)? 3. Which of the seven labels best captures this combination? Format your response EXACTLY as follows, with no extra text before or after: Reasoning: <step-by-step analysis> Final answer: <one label from the list above> C.1.9 Task 3b: CoT F...