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arxiv: 2606.03259 · v1 · pith:ZKWR7NIYnew · submitted 2026-06-02 · 💻 cs.CL

Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent

Raphael Merx , Ekaterina Vylomova , Trevor Cohn This is my paper

Pith reviewed 2026-06-28 10:20 UTC · model grok-4.3

classification 💻 cs.CL
keywords machine translationlarge language modelstranslation adaptationaudience and intentpurpose-driven MTadaptednessself-generated instructionsprompting strategies
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The pith

Explicit instructions enable LLMs to tailor machine translations to audience, tone, and intent, with gains over few-shot examples or context.

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

The paper tests whether large language models can produce translations that match a specific audience, tone, and communicative intent when users supply explicit instructions along with the source text. Evaluation spans 50 languages, five model sizes, and eight domains, showing that such instructions raise adaptedness scores more than alternative prompting strategies. Gains appear largest for informal texts like conversations and social media posts, for bigger models, and for higher-resource languages. Standard automatic metrics often fail to register these improvements and sometimes assign lower scores to the better-adapted outputs. When no hand-crafted instructions exist, the models can generate suitable ones from surrounding document context and recover most of the adaptation benefit.

Core claim

Purpose-adapted machine translation is a measurable capability of large language models. Supplying explicit instructions that name the target audience, tone, and intent produces substantially more adapted translations than treating the task as a fixed source-to-target mapping. This advantage holds across 50 languages and eight domains, exceeds the benefit obtained from semantically matched few-shot examples or paragraph-level context, and is largest on informal domains, with larger models, and in higher-resource languages. Traditional metrics do not track adaptation quality and frequently penalize adapted outputs. Models can also self-generate instructions from document context, closing up t

What carries the argument

Explicit instructions specifying audience, tone, and communicative intent supplied together with the source text.

If this is right

  • Adaptedness gains are larger on informal domains such as conversation and social media than on formal domains.
  • Larger model sizes and higher-resource languages obtain bigger improvements from explicit instructions.
  • Instructions produce higher adaptedness than semantically matched few-shot examples or paragraph-level context.
  • Self-generated instructions from surrounding document context recover up to 80 percent of the adaptedness benefit of curated instructions.

Where Pith is reading between the lines

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

  • Interfaces for translation tools could prompt users for audience and intent details as a default step.
  • New evaluation benchmarks focused on adaptedness would be needed to avoid under-valuing purpose-aware outputs.
  • Professional domains such as marketing or legal translation could integrate purpose specification to reduce post-editing effort.
  • The same instruction-based approach might apply to other text-generation tasks where reader expectations vary.

Load-bearing premise

The human or automatic judgments used to score adaptedness reliably reflect communicative success rather than annotator bias or mismatch with actual reader needs across the tested languages and domains.

What would settle it

A controlled user study in which readers complete a task using the paper's adapted translations versus non-adapted baselines and show no measurable difference in comprehension, engagement, or error rate.

Figures

Figures reproduced from arXiv: 2606.03259 by Ekaterina Vylomova, Raphael Merx, Trevor Cohn.

Figure 1
Figure 1. Figure 1: Full pipeline with Indonesian example: (a) in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Annotation interface in Label Studio. Anno [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Effect of adding user instructions on adaptedness (left) and translation rating (right). Hollow circles = [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average adaptedness ∆ (with − without in￾structions) per domain, for the 27B model. visible to meaning-based scoring: e.g., a Khmer translation rated 100 for meaning preservation but 50 for adaptedness, with the annotator noting “Per￾fect translation, but the tone is too harsh”. 3.4.2 Evaluation model Setup. To scale evaluation, we use an LLM judge (Gemini-3-Flash, full prompt in Appendix E.3) that receive… view at source ↗
Figure 5
Figure 5. Figure 5: Self-instruction pipeline. Gemma-4-31B first [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: WMT24++ results for Gemma-3-27B (997 segments per language), grouped by register richness: the [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The two paragraph-level prompting strategies illustrated on a 3-sentence paragraph. S3: Tout ce que je veux, c’est une d [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of adaptedness scores by instruction variant (Gemma-3-27B, BOUQuET dev). Diamond [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: PEFT pipeline with a 27B teacher. For each English sentence from [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
read the original abstract

Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet MT models and metrics treat translation as a fixed mapping from source to target. LLMs enable users to explicitly specify purpose alongside source text, yet this capability has not been evaluated at scale. We introduce a systematic evaluation of purpose-driven MT across 50 languages, 5 model sizes and 8 text domains. We find that (1) explicit instructions substantially improve translation adaptedness, with larger gains on informal domains (conversation, social media), for larger model sizes and for higher-resource languages; (2) instructions outperform semantically-matched few-shot examples and paragraph-level context; (3) traditional MT metrics fail to capture adaptation quality, often penalizing adapted translations; (4) when curated instructions are unavailable, models can self-generate them from surrounding document context, closing up to 80% of the adaptedness gap to curated instructions. Our results establish that purpose-adapted MT is a viable and measurable capability of LLMs, while highlighting the need for purpose-aware metrics.

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

2 major / 2 minor

Summary. The paper evaluates purpose-driven machine translation with LLMs by providing explicit instructions on audience, tone, and intent. It conducts a large-scale study across 50 languages, 5 model sizes, and 8 domains, claiming that (1) explicit instructions improve adaptedness more than few-shot examples or paragraph context, with larger gains for informal domains, larger models, and high-resource languages; (2) standard MT metrics often penalize well-adapted outputs; and (3) models can self-generate instructions from document context, closing up to 80% of the gap to curated instructions.

Significance. If the adaptedness measurements prove reliable, the work demonstrates a scalable capability for context-sensitive MT that aligns better with real communicative needs than fixed-reference approaches. The breadth of the evaluation (50 languages, multiple domains and sizes) provides useful empirical grounding, and the observation that conventional metrics can penalize adaptations is a constructive signal for future metric research. The self-generation result further suggests practical deployment paths when expert instructions are unavailable.

major comments (2)
  1. [§4–5] Evaluation protocol (§4–5): The central claims rest on judgments of 'adaptedness,' yet the manuscript provides insufficient detail on the exact rating protocol, number of annotators per item, inter-annotator agreement, or statistical significance tests for the reported gains. This information is load-bearing because any systematic bias in the judgments would directly affect the comparative claims about instructions versus few-shot or context.
  2. [Table 2 or equivalent] Results on metric mismatch (Table 2 or equivalent): The claim that traditional metrics 'often penalize' adapted translations requires the actual correlation or delta values between adaptedness scores and BLEU/COMET/etc. to be shown with confidence intervals; without these numbers the assertion remains directional and hard to quantify for the 50-language setting.
minor comments (2)
  1. [Abstract and §5] The abstract states directional findings without effect sizes; the main text should ensure every key comparison (instructions vs. few-shot, self-generated vs. curated) is accompanied by a compact table of mean adaptedness scores and significance markers.
  2. [§6] Clarify whether the 80% gap-closure figure is an average across all languages/domains or a selected subset; report the per-condition breakdown.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and will incorporate clarifications and additional quantitative results in the revised version to strengthen the presentation of the evaluation protocol and metric analyses.

read point-by-point responses

  1. Referee: [§4–5] Evaluation protocol (§4–5): The central claims rest on judgments of 'adaptedness,' yet the manuscript provides insufficient detail on the exact rating protocol, number of annotators per item, inter-annotator agreement, or statistical significance tests for the reported gains. This information is load-bearing because any systematic bias in the judgments would directly affect the comparative claims about instructions versus few-shot or context.

    Authors: We agree that the current description of the human evaluation protocol in §4–5 lacks sufficient detail for full reproducibility and assessment of reliability. In the revision we will expand this section to specify: the exact 5-point adaptedness rating scale and annotator instructions; that each item was rated by three independent annotators; Krippendorff’s alpha values for inter-annotator agreement (reported per domain and overall); and the statistical tests used (paired t-tests with Bonferroni correction) together with p-values for the key comparisons between instruction, few-shot, and context conditions. revision: yes

  2. Referee: [Table 2 or equivalent] Results on metric mismatch (Table 2 or equivalent): The claim that traditional metrics 'often penalize' adapted translations requires the actual correlation or delta values between adaptedness scores and BLEU/COMET/etc. to be shown with confidence intervals; without these numbers the assertion remains directional and hard to quantify for the 50-language setting.

    Authors: We accept that the metric-mismatch claim would be stronger with explicit quantitative support. We will add a dedicated subsection (or expanded Table 2) that reports, across all 50 languages and 8 domains: (i) Pearson and Spearman correlations between human adaptedness scores and each automatic metric (BLEU, COMET, etc.), and (ii) mean deltas in metric scores between adapted and baseline outputs, each accompanied by 95% bootstrap confidence intervals. These numbers will directly quantify the extent and consistency of the penalization effect. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is a large-scale empirical evaluation of instruction-driven MT adaptation across 50 languages, 8 domains and multiple model sizes. It reports comparative results on adaptedness using human and automatic judgments, with no equations, parameter fitting, derivations or self-referential definitions present. All load-bearing claims rest on external measurements rather than reducing to inputs by construction. No self-citation chains or ansatzes are invoked to justify core results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The evaluation rests on standard assumptions from the MT and LLM literature (prompting works, human judgments can proxy communicative success) with no new free parameters, axioms, or invented entities introduced in the abstract.

pith-pipeline@v0.9.1-grok · 5724 in / 1114 out tokens · 25120 ms · 2026-06-28T10:20:19.750414+00:00 · methodology

discussion (0)

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