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Every Part Has Its Role: Part-of-Speech Effects in LLM Post-Training

Laya Iyer, Chen Shani

arXiv:2610.29843v1cs.CLcs.AI

TL;DR

Next-token prediction treats related word choices as distinct targets, and prior concept-augmentation work left open whether the POS expressing a concept affects its value. The paper extends augmentation to verbs, adjectives, and combined POS conditions under the standard objective, finding domain- and POS-dependent outcomes, with verbs yielding the most consistent downstream gains.

  • Problem

    Prior concept-aware post-training focused on nouns, leaving open whether the linguistic category expressing a concept affects the value of concept supervision.

    Source: §1 · p. 1

  • Method

    The study post-trains Llama-3-8B with synonym and hypernym augmentation for nouns, verbs, adjectives, or all three POS categories, retaining the standard next-token objective.

    Source: §1 · p. 1

  • Results

    Augmentation effects vary by POS and post-training domain; verbs provide the most consistent downstream gains, while all-POS augmentation does not reliably outperform targeted augmentation.

    Source: §8 · p. 8

  • Takeaways & Limitations

    The findings motivate POS-aware concept supervision designed around the semantic structure contributed by each linguistic category.

    Source: §8 · p. 8

    Experiments cover only Llama-3-8B and English-language corpora, and do not establish whether the same POS-specific patterns hold under direct concept-level objectives.

    Source: Limitations · p. 9

Abstract

from arXiv · show

Large language models are typically trained with next-token prediction (NTP), which rewards predicting specific tokens rather than explicitly accounting for the semantic similarities between different words or expressions. Prior work has addressed this limitation by augmenting training data with minimally varying sentences that replace a target word with conceptually related alternatives, e.g., "The tired coach ran home" $\rightarrow$ {"The tired trainer ran home", "The tired instructor ran home", ...}. Such concept augmentation improves performance, but it remains unclear whether its effectiveness depends on where in the sentence the concept is expressed. We reproduce and extend prior work $\textit{beyond nouns}$ to verbs and adjectives, asking whether the part of speech (POS) carrying the concept signal affects perplexity and downstream performance. We find different patterns across evaluation settings: concept augmentation improves perplexity most consistently on news, independent of the POS used, whereas verbs provide the most reliable downstream gains. Despite these behavioral differences, representational analyses using CKA and linear probing show that the conditions yielding the largest downstream gains are not necessarily those that most alter the model's internal representations. Together, these results suggest that the effectiveness of concept augmentation depends on where and how conceptual information is introduced during training.

1 Introduction

The paper asks whether the part of speech carrying conceptual variation affects concept-supervised training, extending prior noun-focused work to verbs, adjectives, and combined POS conditions. It finds that POS shapes outcomes: verbs provide the most consistent downstream gains, while the strongest POS varies across domains and tasks.

1 Introduction

  • Prior concept-aware post-training work used synonym and hypernym supervision but focused on nouns, leaving open whether the linguistic category expressing a concept changes the value of that supervision.Nouns, verbs, and adjectives encode entities and categories, actions and relations, and properties and states, respectively.Source: §1 · p. 1
  • The study extends prior noun-only augmentation to verbs, adjectives, and all three POS categories while retaining next-token prediction to isolate where conceptual variation is introduced.It post-trains Llama-3-8B across arXiv, news, and YouTube data and evaluates held-out perplexity and seven downstream NLP tasks.Source: §1 · p. 1
  • Conceptually related substitutions can vary a noun, verb, adjective, or all three POS categories in the same input sentence.Figure 1 illustrates synonym and hypernym substitutions using context-aware or dictionary-based extraction.Source: Figure 1 · p. 2
  • Verb augmentation provides the most consistent downstream gains, but the strongest POS varies across domains and tasks, and augmenting all POSs together does not reliably outperform targeted augmentation.These findings motivate treating POS as an additional design axis for concept supervision.Source: §1 · pp. 1–2

2 Related Work

Related work examines conceptual structure in language modeling, from critiques of token-level next-token prediction to methods that add concept-level representations or supervision. The paper's question is complementary: whether POS-specific data augmentation affects outcomes under the standard next-token objective.

2 Related Work

  • Prior work questions whether next-token prediction alone captures generalizable semantic structure; Shani et al. (2023) and Zhang et al. (2026) discuss how token-level targets can fragment or compete across semantically related alternatives.The paper retains next-token prediction and tests whether structured variation in training data changes what is learned.Source: §2 · p. 2
  • Concept-level approaches add structure at different pipeline stages, including sentence-level embeddings, multi-token units, continuous concept representations, and joint prediction of latent concepts and tokens.These approaches differ in what counts as a concept but seek supervision above individual tokens.Source: §2 · p. 2
  • Iyer et al. (2026) use synonyms and hypernyms in post-training, while Zhang et al. (2026) optimize probability over contextually equivalent token sets; this study instead retains standard next-token prediction and uses augmentation to isolate POS effects.The comparison covers perplexity, downstream transfer, training dynamics, and representation geometry.Source: §2 · p. 2

3 Data Processing and Models

The study adapts concept-aware post-training to four POS conditions and multiple corpora, using synonym and hypernym substitutions from context-aware or dictionary-based sources. Matched vanilla models control for training volume, while the reported perplexity pattern varies by corpus.

3.2 Concept Models

  • Following Iyer et al. (2026), the study focuses on data augmentation rather than a modified concept-level objective and further trains Llama-3-8B under standard next-token prediction.Separate models are trained for each POS, corpus, semantic relation, and concept-source condition.Source: §3 · p. 3, §3.2 · p. 3

3.1 Data & Concept Supervision Extraction

  • The study uses arXiv abstracts, news, YouTube comments, and a combined corpus, targeting nouns, verbs, adjectives, or all three POS categories.Each target POS receives synonym or broader hypernym alternatives, extracted either by a context-aware LLM or from WordNet without sentence context.Source: §3.1 · p. 3

3.3 Baselines

  • Each augmented model is compared with a size-matched vanilla model trained on the same source sentences, repeated as needed so both conditions have equal training volume.This makes conceptual substitution, rather than total training volume, the intended difference between the conditions.Source: §3.3 · pp. 3–4
  • News post-training shows the most consistent perplexity improvement (+5.5%), while augmentation decreases performance on arXiv (−8.2%); the relative POS effect also changes across corpora.Figure 3 reports improvement relative to matched vanilla models, with positive values indicating lower perplexity.Source: Figure 3 · p. 4

4 Experimental Setup

The evaluation measures held-out perplexity and downstream transfer, reporting results relative to matched vanilla models where applicable. Downstream assessment uses LoRA sequence classification across seven benchmarks, with held-out test accuracy as the primary metric.

4 Experimental Setup

  • The evaluation combines held-out perplexity with transfer on seven downstream classification benchmarks, reporting results relative to matched vanilla models where applicable.Validation-based early stopping is also used to analyze fine-tuning dynamics.Source: §4 · p. 4

4.1 Perplexity

  • Perplexity improvement is the matched vanilla model's perplexity minus the concept model's perplexity, so positive values indicate lower, better perplexity.The accuracy comparison is the concept model's accuracy minus the corresponding vanilla model's accuracy.Source: §4.1 · p. 4

4.2 Downstream Evaluation

  • The seven downstream tasks use LoRA-based sequence classification instead of the prior instruction-style fine-tuning procedure, with held-out test accuracy as the primary metric.The checkpoint with the lowest validation loss is selected; weighted precision, recall, F1, and AUC-PR are also reported.Source: §4.2 · p. 4

5 Results

The results reproduce the broad noun-only trends, but show that perplexity and downstream gains depend on post-training domain, POS, and augmentation choices. Verbs are the most consistently effective POS downstream, while perplexity is only a partial proxy for transfer.

5.1 Reproducibility Reality Check

  • The noun-only results reproduce the same broad perplexity and downstream trends reported by Iyer et al. (2026), with small differences expected from data reprocessing and revised LoRA fine-tuning.The reproduction serves as a sanity check for the expanded experiments.Source: §5.1 · pp. 4–5

5.3 Perplexity and Downstream

  • Perplexity and downstream accuracy are moderately correlated for context augmentation (r = 0.38, p = 0.033), but the relationship is weaker for dictionary augmentation (r = 0.30, p = 0.096).Models appear in all four quadrants, so lower perplexity does not reliably translate into higher downstream accuracy.Source: Figure 4 · p. 5, §5.3 · p. 6

5.2 Perplexity

  • Perplexity improves by 5.5% on news but worsens by 8.2% on arXiv, with smaller average decreases of 2.6% on YouTube and 2.5% on the combined corpus.Thus, the same augmentation procedure can improve or worsen perplexity depending on the post-training domain.Source: §5.2 · p. 5
  • No POS consistently lowers perplexity: its effects depend on domain and semantic relation, with news generally benefiting and adjectives producing the largest gains there.For hypernyms, adjective and verb augmentation reduce news perplexity by 14.4% and 13.8%; on YouTube, synonym verb augmentation improves perplexity by 12.6%.Source: §5.2 · p. 5

5.4 Downstream Performance

  • Verb augmentation gives the best mean downstream result in six of eight semantic-relation×corpus settings, but no single strategy wins globally.The strongest condition varies with corpus, semantic relation, concept source, and POS; all-POS augmentation does not consistently beat the strongest targeted POS.Source: Figure 5 · p. 6, §5.4 · pp. 6–7
  • Downstream gains vary substantially by task: GLUE has the largest reported swings, while Spam, Hate Speech, and Emotion are comparatively stable.GLUE gains include hypernym verbs on arXiv (+30.9) and synonym adjectives on news (+28.8); Logical Fallacy is also sensitive to augmentation choice.Source: §5.4 · p. 6

6 Analyses

The analyses separate fine-tuning dynamics from representation change: POS is associated with when fine-tuning stops, corpus with early learning speed, and the largest representational changes do not identify the strongest transfer condition.

6.1 Training Dynamics

  • Adjective-augmented models stop significantly earlier than all-POS models (p = 0.001), while combined-corpus models improve faster than YouTube models between fine-tuning steps 250 and 500 (p = 0.004).The stopping-point comparison is grouped by augmented POS; the early accuracy-gain comparison is grouped by post-training corpus.Source: Figure 6 · p. 7, §6.1 · p. 7, Figure 7 · p. 7

6.2 Representational Analysis

  • CKA shows that all-POS models diverge most from the base model at the final layer (0.743), while verb models remain most similar (0.810).Post-training corpus also matters: arXiv models show the largest divergence (0.751), and greater divergence is associated with lower further-training loss but higher held-out perplexity.Source: §6.2 · pp. 7–8
  • All-POS augmentation produces the clearest concept-specific probe effect, improving dictionary-model probe accuracy by about 0.17 percentage points over vanilla across layers and about 0.45 points at the final probed layer.Much of the general increase in linearly decodable POS information also occurs in matched vanilla models.Source: §6.2 · p. 8
  • The CKA and probing analyses identify all-POS augmentation as producing the largest representational change, although it is not the strongest downstream condition.Both analyses characterize internal representations after post-training; fine-tuning trajectory measures were standardized within each downstream task.Source: §6.1 · p. 7, §6.2 · p. 8

7 Discussion

Concept augmentation’s usefulness depends on where conceptual variation enters: verb augmentation transfers most consistently, but broader POS coverage and larger representational changes do not guarantee better downstream performance.

7 Discussion

  • All-POS models change most under CKA, while verb models remain comparatively close to the base model and provide stronger downstream performance.This contrast suggests that the amount of representational change is not itself a measure of transfer usefulness.Source: §7 · p. 8
  • Greater representational divergence is associated with lower further-training loss but higher held-out perplexity, distinguishing post-training fit from preserved transferable structure.The discussion relates this pattern to post-training specialization, citing Bachmann and Nagarajan (2025).Source: §7 · p. 8
  • All-POS augmentation yields the clearest concept-specific probe increase but not the strongest downstream performance; verb augmentation transfers well without unusually large POS-probe gains.The authors suggest the probe may be too coarse to explain behavioral differences and that usefulness depends more on what information changes than how much.Source: §7 · p. 8

8 Conclusion

The paper extends noun-focused concept augmentation to verbs and adjectives while retaining next-token prediction, finding that POS and post-training domain shape its effects.

8 Conclusion

  • Extending noun-focused experiments to verbs, adjectives, and all three POS categories, the study finds that effects depend on POS and post-training domain.Verb augmentation gives the most consistent downstream gains, while all-POS augmentation does not reliably outperform targeted augmentation.Source: §8 · p. 8
  • Larger representational changes do not necessarily yield greater utility: all-POS models change most under CKA and probing, while verb models transfer more consistently and remain closer to the pretrained representation.The findings motivate POS-aware concept supervision designed around the semantic structure contributed by each linguistic category.Source: §8 · p. 8

Limitations

The experiments are limited to Llama-3-8B and English-language corpora, and use data augmentation rather than direct concept-level objectives. POS and semantic-relation extraction quality also varies, with adjective hypernymy less well-defined than noun or verb hypernymy.

Limitations

  • The study covers only Llama-3-8B and English-language corpora, so POS-specific effects may differ at other scales, architectures, or languages.It also studies data augmentation only and does not establish whether the same POS-specific patterns hold under direct concept-level objectives.Source: Limitations · p. 9
  • Concept extraction quality varies across POS and semantic relations, and adjective hypernymy is less well-defined than noun or verb hypernymy.Context-aware generation and dictionary extraction introduce different forms of noise that may partly explain the observed domain- and POS-specific effects.Source: Limitations · p. 9

Ethical Considerations

Concept substitutions can distort context and may amplify stereotypes or spurious associations; deployment-related harms were not evaluated. The additional sections document the noun-only reproducibility check, absolute perplexities, downstream fine-tuning procedure, and downstream comparison.

Ethical Considerations

  • Concept substitutions can distort context or amplify stereotypes; the study does not evaluate deployment-related harms, and authors report reviewing LLM-assisted manuscript edits.The authors recommend inspecting concept sets, especially for demographic or otherwise sensitive content.Source: Ethical Considerations · p. 9

A Reality Check

  • The noun-only experiments recover Iyer et al. (2026)'s broad trends, with differences attributed to data reprocessing and revised downstream fine-tuning.Jointly identifying noun, verb, and adjective targets changes the eligible noun targets; downstream differences may also reflect LoRA-based sequence classification.Source: A Reality Check · p. 10

B Additional Perplexity Results

C Downstream Fine-Tuning Details

  • The seven downstream classification tasks use LoRA adapters and a randomly initialized classification head while keeping backbone weights frozen.The setup uses rank 16 adapters on q_proj and v_proj, early stopping on validation loss, and held-out test accuracy as the primary metric.Source: C Downstream Fine-Tuning Details · pp. 10–11

D Downstream Results

  • Verb augmentation is strongest in six of eight relation-corpus conditions in the mean accuracy-change comparison against matched vanilla models.Table 1 averages context-aware and dictionary-based variants; Table 2 instead averages across POS to compare concept sources.Source: Table 1 · p. 11, D Downstream Results · p. 11, Table 2 · p. 11

E Training-Dynamics Details

Training-dynamics analyses compare early accuracy gains and validation-selected stopping points. CKA and linear probing show that representational change varies by POS and does not align simply with downstream transfer; the resource accounting totals approximately 700 GPU-hours.

E Training-Dynamics Details

  • Adjective-augmented models stop earlier than all-POS models (p = 0.001), and verb and adjective conditions differ (p = 0.006).Combined-corpus models improve more rapidly than YouTube models between steps 250 and 500 (p = 0.004); no comparably strong POS-level early-slope difference appears.Source: E Training-Dynamics Details · pp. 11–12

F.1 Centered Kernel Alignment

  • All-POS models diverge most under CKA while verb models remain closest; dictionary-based conditions have lower mean CKA, and greater divergence tracks lower training loss but higher held-out perplexity.The reported associations are ρ = −0.38, p = 0.008 for further-training loss and ρ = 0.35, p = 0.015 for held-out perplexity.Source: §F.1 · p. 12, Table 3 · p. 12, Table 5 · p. 12

F.2 Linear Probing

  • Further post-training increases later-layer POS-probe accuracy across variants, while all-POS augmentation has the clearest additional probe effect over vanilla and verbs transfer more consistently.All-POS models show the strongest representational changes without the strongest downstream performance; verb models gain consistently without a similarly large increase in coarse POS decodability.Source: Table 6 · p. 12, §F.2 · p. 13, Table 4 · p. 13

G Computational Resources

  • Approximately 700 GPU-hours were used across the 96 models: about 103 for post-training and 595 for downstream fine-tuning.Post-training uses five GPUs per run; downstream evaluation uses one A100-SXM4-80GB GPU.Source: G Computational Resources · p. 13
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