GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction

Jie Zhao; Lishuang Li; Wanting Ning; Yubo Feng; Yuxiao Fei

REVIEW 4 major objections 4 minor 36 references

GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read GDLLM reports 87.5 micro-F1 on TB-Dense and 90.9 on MATRES by routing LLM probability distributions through a two-layer graph attention network.

desk verdict Solid new idea—LLM softmax vectors as GAT edge features—but single-run results and a thin evaluation make the SOTA claim unproven. read the letter →

arxiv 2508.20828 v1 pith:3GOGVDTL submitted 2025-08-28 cs.CL cs.IR

Jie Zhao , Wanting Ning , Yuxiao Fei , Yubo Feng , Lishuang Li This is my paper · ORCID

classification cs.CLcs.IR

keywords eventtemporalrelationextractionlargelanguagemodelsgraphattentionnetworkssoftprobabilityedgefeatureslong-distancedependenciesminorityclassesMATRESTB-Dense

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper aims to show that event temporal relation extraction improves when a large language model is used as a soft evidence generator rather than a hard reasoner. The proposed method, GDLLM, takes the probability distribution an LLM assigns to each event pair's temporal relation and uses it as edge features in a graph attention network, allowing information to propagate across events that are far apart in the text. On TB-Dense and MATRES it reports micro-F1 scores of 87.5 and 90.9, above the previous state of the art, and it reports that minority relation classes benefit the most. This matters because temporal relations in real text are imbalanced and long-distance event pairs are exactly the hard cases for prompt-based LLM approaches.

What carries the argument

The load-bearing mechanism is the distance-aware graph attention module with soft-inference edge features. Events are nodes; each node feature encodes the event's order and type. Every edge carries the LLM's probability distribution over relation classes instead of a hard predicted label. A two-layer multi-head graph attention network with eight heads computes attention coefficients that depend on the source node, target node, and this probability vector, allowing the network to weigh evidence from distant events. The second layer's head outputs are averaged, and the final classifier concatenates the two node representations with the original probability vector, splitting the global feature

What would settle it

Take the trained GDLLM model on MATRES and delete the direct probability vector from the final classifier, keeping only the two graph node representations; if micro-F1 remains near 90.9, the graph is not adding information beyond the LLM probabilities. A complementary check is a graph-free baseline that feeds the LLM probability vector through a linear layer and compares its micro-F1.

Watch

Extended reading notes

Core claim

The paper's central claim: the bottleneck in event temporal relation extraction is not the language model's knowledge but how its outputs are used. GDLLM fine-tunes an LLM with LoRA to emit a probability vector over relation classes for each event pair, then uses those vectors as edge features in a two-layer, eight-head graph attention network over a document-level event graph. Attention weights combine node features with the probability vector, so soft evidence propagates between distant events while the same vector stays in the final classifier for short-distance pairs. With Llama3.1-8B this yields 87.5 micro-F1 on TB-Dense and 90.9 on MATRES, 1.9 and 0.5 points above the previous best, wi

Load-bearing premise

The load-bearing premise is that the LLM's probability distribution over relation classes carries reliable relational evidence and that the graph attention module adds non-redundant structure on top of it; if the graph output merely echoes those probabilities, the reported gains would collapse.

Editorial extensions

If this is right

LLM-based event temporal relation extraction can avoid hand-written prompt templates entirely; probability outputs become the interface between the language model and downstream structure.
Minority temporal relations improve enough that the micro-macro gap narrows, so downstream applications needing rare relations such as SIMULTANEOUS would see better recall.
The method transfers across LLMs: Qwen2.5-7B also beats the previous state of the art, suggesting the graph component is not tied to a single model.
Even in zero-shot mode, with no LoRA fine-tuning and fixed graph parameters, the approach outperforms earlier manually prompted zero-shot systems on MATRES.
Removing the graph module hurts more than removing the soft-probability edge features, indicating long-distance event pairs are the main source of error this architecture recovers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A plausible testable consequence is that the base LLM matters less than the calibration of its probability outputs; a smaller, well-calibrated model might capture much of the gain at lower cost.
The paper does not isolate whether the graph's benefit comes from multi-head attention or from the probability edge features; replacing GAT with a distance-weighted transformer would separate structural propagation from attention.
Because the final classifier concatenates the raw probability vector alongside graph outputs, a stronger ablation would remove that direct path to show whether the graph genuinely adds information or just reweights the LLM's own distribution.
The distance-5 result shows only a small gain over the version without soft inference, suggesting very long event chains may need a different inductive bias such as hierarchical or recurrent graph layers.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a plausible, well-motivated architecture paper with a real new idea, but the empirical evidence as reported is not enough to support the SOTA claim. The novelty is using LLM softmax probability vectors as edge features in a multi-head GAT for event temporal relation extraction. That specific combination is not in the cited literature, and the ablations suggest it matters: replacing soft probabilities with hard 0/1 labels (w/o PI) drops 8-11 F1, and removing the GAT entirely (w/o GD) drops 12-13. The distance-stratified analysis in Table 4 supports the claim that GAT helps on long-distance pairs, which is the mechanism they advertise. So the paper earns credit for a clear and interesting design and for testing its components.

The soft spots are mostly about statistical credibility. All results are single runs. MATRES gain over MulCo is 0.5 F1, which can easily be seed noise. The paper doesn't report variance, number of seeds, or whether HEBO selects hyperparameters on a validation split or directly on the test set. If the latter, the numbers are optimistically biased. No code is released. These issues directly undermine the SOTA claim. The w/o LP ablation (removing the LLM probabilities entirely) drops 22 points, but that is an expected sanity check rather than a meaningful comparison. Also, the final classifier directly concatenates the raw probability vector, so the GAT's structural contribution is not isolated from the value of having the probability as a feature. The paper does show w/o GD is much worse, which suggests the graph/event-node features matter, but an additional control (e.g., GAT node features without edge probabilities, or a non-graph aggregator) would sharpen the claim.

One wording misstep: they claim to work 'without manually designed prompts or instructions', but the event markers [EVi] are manually defined and the zero-shot experiments use a hand-built prompt. That's a minor overstatement.

The paper is worth refereeing, but not as-is. I'd ask for multiple seeds with standard deviations, a clear description of the tuning protocol, code or at least full hyperparameters, and an analysis that isolates the GAT's contribution beyond directly using the probability vector. With those, the core idea could be a solid contribution. For a reader working on structured prediction with LLMs, the architecture is worth knowing about.

Referee Report

4 major / 4 minor

Summary. The paper proposes GDLLM, a two-stage method for event temporal relation extraction. A LoRA-fine-tuned LLM (Llama3.1-8B or Qwen2.5-7B) produces a probability distribution over relation classes for each event pair; these probability vectors are used as edge features in a two-layer, 8-head GAT built over event nodes in a document. The final classifier concatenates the GAT output representations with the LLM probability vector and classifies via a fully connected layer and softmax. The method is evaluated on TB-Dense and MATRES, reporting micro-F1 scores of 87.5 and 90.9 with Llama3.1-8B, which the paper claims are state-of-the-art. The paper also includes ablations (w/o LP, w/o GD, w/o PI), a distance-conditioned analysis, a zero-shot comparison, t-SNE visualizations, and a comparison with GNN-based benchmarks.

Significance. If the reported results are reliable, the core idea—using an LLM's probability distribution as soft edge features in a GAT to capture both long-distance and short-distance temporal dependencies—is a meaningful and well-motivated contribution. The design is sensible, the distance-conditioned analysis in Table 4 provides qualitative support, and the paper is transparent about some limitations, such as the Qwen model's weakness on the EQUAL class. The main significance is limited by the absence of statistical reliability evidence: the MATRES gain over the prior SOTA is only 0.5 micro-F1, and a single run cannot establish superiority. The TB-Dense gain of 1.9 points is larger but still needs variance estimates.

major comments (4)

[§3.3, Table 1] The central SOTA claim on MATRES rests on a 0.5-point micro-F1 improvement over MulCo (90.9 vs 90.4). The paper reports one run per configuration with no standard deviation, no number of seeds, and no significance test. Neural models on MATRES typically exhibit seed variance of at least ±0.5 F1, so this margin may be noise. Please report mean ± std over multiple seeds (e.g., 5) and a paired significance test for the comparisons with MulCo, and also for the TB-Dense results.
[§3.2, HEBO] The hyperparameter optimization procedure is described only as 'we employ the HEBO algorithm.' It is not stated whether HEBO optimizes on a fixed validation split or directly on the test set, what the search space is, or which final hyperparameters were selected. If test-set performance was used for model selection, the reported numbers are optimistically biased and not directly comparable to baselines. Please specify the validation protocol and report the final hyperparameters (learning rate, weight decay, batch size, GAT hidden dimensions, etc.).
[§3.5, Tables 2 and 3] The 'w/o LP' ablation removes the entire LLM component, not just the probability generation: the GAT then receives only the dataset-derived node features. The resulting drop (e.g., from 90.9 to 68.7 on MATRES) shows that the LLM is essential, but it does not isolate the contribution of the probabilistic edge features. The 'w/o PI' ablation partly addresses this by replacing probabilities with hard labels, but the description of 'w/o LP' should be revised so readers do not interpret it as a controlled ablation of probability information.
[§3.1, §3.2] The evaluation protocol is not fully reproducible: data splits are said to follow prior work, but no split details are given, and no code or configuration is released. Given that the contribution is an empirical pipeline and the SOTA claim is quantitative, please provide a detailed setup (data splits, event marker preprocessing, LoRA configuration, and GAT layer specifics) and consider releasing code to allow independent verification.

minor comments (4)

[§2.2, Eq. (1)] The notation in Eq. (1) is inconsistent: the numerator uses e^{z_r} with index r, while the denominator sums e^{z_n} over n=1..c. Please unify the index notation. Also, 'in the inference process of LoRA tuning' is awkward; the probability is generated after fine-tuning, not during inference of LoRA tuning.
[§2.3, Graph feature construction] The node feature construction is vague: 'Each event Ei and its order and type information are both represented as a node' and 'node features are obtained from the dataset corresponding to the event.' Please specify how event order, event type, and any contextual embeddings are encoded into h_i^(0).
[§3.7, Figure 5] The main claim says the method works 'without manually designed prompts or instructions,' but the zero-shot experiments use hand-crafted prompts. This is not contradictory for the main pipeline, but the text should clarify that the prompt-free claim applies to the fine-tuned GDLLM pipeline, not to the zero-shot setting.
[Throughout] There are several typos and formatting issues: 'realtion', 'Manully' in Figure 5, 'V anilla', and inconsistent capitalization in figure/table captions. Please proofread.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the pipeline is a standard two-stage supervised design with ablations that isolate each component, and the only self-citation is non-load-bearing.

full rationale

GDLLM is an empirical supervised pipeline rather than a formal derivation. The LoRA-fine-tuned LLM produces probability vectors p_ij (Eq. 1); the GAT uses these vectors as edge features (Eqs. 2-5), and the final classifier concatenates h_i, p_ij, h_j (Eq. 6). This is stacking of a fitted LLM with a GAT, not an equivalence-by-construction: the paper's ablations show each component contributes independently (w/o LP drops MATRES F1 to 68.7, w/o GD to 78.1, w/o PI to 82.6 vs 90.9 for the full model). The final classifier's direct use of p_ij is an explicit design choice, not a hidden reuse that makes the output equal to the input: the GAT adds a structural transformation over the document graph that is empirically nontrivial. The only self-citation is the authors' earlier TCT (Ning et al. 2024), used as a baseline and as a reference for the standard data split and micro-F1-with-VAGUE-excluded metric; it is not invoked as a uniqueness theorem or as justification for the method's core mechanism. No ansatz is smuggled via citation and no known result is merely renamed. The fragility of the MATRES SOTA margin (0.5 F1 over MulCo) is a statistical/evaluation concern, not a circularity concern.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rest on standard empirical assumptions about model design and evaluation. No new theoretical entities or physical constants are introduced. The most significant reliance is on the domain assumptions about the informativeness of LLM probabilities and the GAT's ability to exploit them.

free parameters (5)

LoRA rank = 16
Set by hand (Section 3.2), not derived or optimized; a standard hyperparameter.
Number of GAT layers = 2
Chosen in Section 2.3 without a search or justification.
Number of attention heads per GAT layer = 8
Chosen in Section 2.3 without a search or justification.
Hidden dimensions (dh, dh1, dh2) = not reported
Tuned via HEBO (Section 3.2) but final values are not disclosed, preventing replication.
Learning rate, weight decay, batch size = not reported
Tuned via HEBO but not listed in the paper.

assumptions (5)

domain assumption LLM-generated probability distributions are more accurate than SLM-generated ones
Figure 3 shows only two random samples and is used as evidence for choosing LLMs as the base model.
domain assumption GAT with multi-head attention can effectively capture long-distance dependencies between events
Section 2.3 posits this without a formal proof or comparison against other distance-aware mechanisms.
domain assumption Probability distributions as edge features are more informative than 0/1 hard labels
Section 2.3 introduces this as the 'soft inference' paradigm; the ablation w/o PI supports it empirically on two datasets, but no theoretical justification is given.
domain assumption The distance between events defined as the number of intervening events is a meaningful proxy for dependency difficulty
Section 3.6 uses this definition to claim distance-aware improvements, but the paper does not validate the definition against other distance measures.
domain assumption Micro-F1 with VAGUE excluded is the standard evaluation metric
Section 3.1 follows prior work; this is a conventional but not universally agreed choice.

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Cite this review

Pith. "Pith review of GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction." pith.science (2026). https://pith.science/paper/3GOGVDTL

@misc{pith2026250820828,
  author       = {Pith},
  title        = {Pith review of: GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3GOGVDTL}},
  note         = {Machine review of arXiv:2508.20828}
}

read the original abstract

In Natural Language Processing(NLP), Event Temporal Relation Extraction (ETRE) is to recognize the temporal relations of two events. Prior studies have noted the importance of language models for ETRE. However, the restricted pre-trained knowledge of Small Language Models(SLMs) limits their capability to handle minority class relations in imbalanced classification datasets. For Large Language Models(LLMs), researchers adopt manually designed prompts or instructions, which may introduce extra noise, leading to interference with the model's judgment of the long-distance dependencies between events. To address these issues, we propose GDLLM, a Global Distance-aware modeling approach based on LLMs. We first present a distance-aware graph structure utilizing Graph Attention Network(GAT) to assist the LLMs in capturing long-distance dependency features. Additionally, we design a temporal feature learning paradigm based on soft inference to augment the identification of relations with a short-distance proximity band, which supplements the probabilistic information generated by LLMs into the multi-head attention mechanism. Since the global feature can be captured effectively, our framework substantially enhances the performance of minority relation classes and improves the overall learning ability. Experiments on two publicly available datasets, TB-Dense and MATRES, demonstrate that our approach achieves state-of-the-art (SOTA) performance.

Figures

Figures reproduced from arXiv: 2508.20828 by the authors.

**Figure 2.** Overall architecture of our proposed method. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The distribution of probabilities generated by different language models on the TB-Dense (a) and the [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: The performance of micro-F1, macro-F1, and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: The micro-F1 score of the performance com [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: The visualized clustering comparison results [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: The micro-F1 score of the previous GNNbased method versus our approach on the two datasets. “MulCo-RGAT(n)” represents the model adopts n GNN layers. 5 Conclusion In this paper, we propose GDLLM, a Global Distance-aware modeling approach based on LLMs. Specifically, w…

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen Mckeown, and Yaser Al-Onaizan. 2020. Severing the edge between before and after: Neural architectures for temporal ordering of events. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages...

work page 2020

[4] [4]

Taylor Cassidy, Bill McDowell, Nathanael Chambers, and Steven Bethard. 2014. An annotation framework for dense event ordering. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 501--506

work page 2014

[5] [5]

Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, and 1 others. 2024. Do not think that much for 2+ 3=? on the overthinking of o1-like llms. arXiv preprint arXiv:2412.21187

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