G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models

Jiafeng Guo; Long Bai; Tat-Seng Chua; Xiaolong Jin; Xueqi Cheng; Zixuan Li

REVIEW 4 major objections 3 minor 30 references

G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models

T0 review · 4 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Anonymized time-graphs lift LLM forecasting in zero-shot tests

desk verdict A useful two-stage recipe for LLM-based TKG forecasting, but the anonymization claim is confounded by extra training data and a missing zrLLM baseline. read the letter →

arxiv 2506.00445 v1 pith:GNAHJJWL submitted 2025-05-31 cs.CL

Long Bai , Zixuan Li , Xiaolong Jin , Jiafeng Guo , Xueqi Cheng , Tat-Seng Chua This is my paper

classification cs.CL

keywords temporalknowledgegraphforecastinglargelanguagemodelsgeneralizationanonymizationin-contextlearningsupervisedfine-tuningzero-shotlow-resource

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

The paper claims that LLM-based temporal knowledge graph forecasting fails to generalize across domains because the model learns two kinds of knowledge at once: reusable temporal patterns and scenario-specific facts (who did what, under which relation, at which time granularity). It proposes a General-to-Specific framework (G2S) that first trains on anonymous temporal structures—entities, relations, and timestamps replaced by abstract IDs—from GDELT and WIKI, and only later injects the concrete entities and relations of the target dataset through in-context learning or fine-tuning. The result is a forecast model that outperforms existing ICL and SFT baselines under zero-shot, low-resource, and standard settings on ICEWS14, ICEWS18, and YAGO. If true, this separates a transferable 'grammar' of temporal events from the vocabulary of a particular scenario, making LLM forecasters cheaper to adapt to new domains.

What carries the argument

The carrying object is the anonymous temporal structure (ATS): a query-time-relative rendering in which each history line is "A(t):[A(s),A(r),A(o)]" with $A(t) = t_q - t$, and entities/relations mapped to IDs by one of three strategies (frequency rank, global dataset ID, or random ID). The general learning stage fine-tunes an LLM (8B) on these ATS samples from GDELT and WIKI, forcing it to learn structural patterns independent of names; the specific learning stage prepends entity/relation ID mappings and continues with ICL or SFT. The relative timestamp (query time = 0) is what lets patterns from 15-minute-granularity GDELT transfer to daily ICEWS and yearly YAGO.

What would settle it

Train a control model on the same GDELT+WIKI samples but with raw, non-anonymized facts (or with a fixed random ID mapping shared between training and target datasets), and measure zero-shot ICEWS14 performance; if the control matches G2S, the improvement is not caused by anonymized structure learning. Alternatively, a dataset with a radically different schema where anonymous patterns cannot transfer should show no zero-shot gain, which would bound the claim.

Watch

Extended reading notes

Core claim

The central claim is that temporal knowledge graphs contain two entangled kinds of knowledge—general patterns (invariant structures such as recurring periodic sequences like [1,1,2],[1,1,4],[1,1,2] → [1,1,4]) and scenario information (the concrete entities, relations, and time granularity). G2S disentangles them by anonymizing the scenario information into abstract IDs during a general learning stage, then restoring it in a specific learning stage. Evidence: zero-shot variants trained only on GDELT and WIKI achieve accuracy close to or above a 20B-parameter ICL model, and fine-tuned G2S beats or matches baselines on three benchmarks. The paper interprets these gains as confirming that separate learning stages prevent interference between general patterns and scenario information.

Load-bearing premise

The load-bearing premise is that anonymizing entities, relations, and timestamps into abstract IDs removes exactly the scenario-specific information while preserving the generalizable temporal structure; if the zero-shot gains instead come from the extra 130,000 training samples or from latent leakage through ID-frequency correlations, the disentanglement story collapses.

Editorial extensions

If this is right

If the general stage truly learns transferable patterns, a single anonymous-pattern pretrained model should adapt to new TKG domains with only a handful of example mappings, reducing the labeled data needed for forecasting.
The framework implies that ICL and SFT are complementary rather than competing: the general stage pretrains on structure, and the specific stage can switch modes depending on data availability.
On ICEWS18 the paper notes G2S slightly underperforms GenTKG, attributing it to multi-token ID generation; fixing single-token decoding for global IDs should close that gap.
The anonymization strategies matter: global IDs work best with enough data, while frequency IDs are better in low-resource settings, suggesting practical guidance for choosing a strategy per dataset.

Reading between the lines

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

Editorial inference: the same anonymize-then-specialize recipe could apply to other LLM sequence tasks with a procedural core and a lexical surface—e.g., event extraction, procedural planning, or code synthesis—where the general pattern (control flow) is masked from domain names.
Editorial inference: the paper's relative-time encoding (query time = 0) suggests that future work could test whether shifting from absolute calendar time to relative intervals is itself the main transfer mechanism, independent of entity anonymization.
Editorial inference: a testable extension would be to train the general stage on purely synthetic anonymous temporal structures with known planted patterns; if G2S improves as planted pattern complexity increases, the causal link between structure-learning and generalization strengthens.

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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

The core idea here is sensible: separate the learning of general temporal patterns from scenario-specific facts by first fine-tuning on anonymized structures, then adapting to the target dataset. That framing is clean, and the paper does some things well. The relative timestamp encoding (Eq. 2) is a simple improvement over prior absolute-time IDs. The systematic comparison of FID, GID, and RID anonymization strategies is new and practically useful. The writing is clear, the experimental coverage (standard, zero-shot, low-resource) is appropriate, and the authors are upfront about limitations like the multi-token ID problem and the one-hop history selection.

Where the paper falls short is the evidence for its central claim. The zero-shot comparison pits G2S, which fine-tunes on 100k-130k general-stage samples, against ICL baselines that use no fine-tuning at all. The gain could come from exposure to more TKG-forecasting data, not from anonymization. The w/o GL ablation removes the entire general stage, so it does not control for data volume. The closest control, G2SGL(F+Map), adds scenario information during general learning and actually matches or slightly beats G2SGL(F) on the schema-overlapping ICEWS14/18. That cuts against the interference hypothesis and suggests dataset-specific transfer (GDELT and ICEWS share the CAMEO relation schema) may be doing the work.

Also notable: the paper cites zrLLM (Ding et al., 2024) but never compares against it, which is a real gap for a zero-shot claim. Minor issues: no code release, and anonymization strategies are selected on validation, which is standard but should be flagged.

All that said, this is an honest, well-structured empirical paper, not a circular one. The results are genuine test-set forecasts, and the framework is a practical contribution to LLM-based TKG forecasting. The main confound is addressable with a control that trains on the same general-stage data without anonymization, plus a zrLLM comparison. With those, the paper would be much stronger. I would send it to a serious referee, and I would expect a revision rather than a desk reject.

Referee Report

4 major / 3 minor

Summary. The paper proposes G2S, a two-stage framework for temporal knowledge graph (TKG) forecasting with large language models. In the general learning stage, the model is fine-tuned on anonymized temporal structures from GDELT and WIKI, where entities, relations, and timestamps are replaced by abstract IDs. In the specific learning stage, scenario information is injected either via in-context learning or supervised fine-tuning. The framework is evaluated on ICEWS14, ICEWS18, and YAGO under standard, zero-shot, and low-resource settings, reporting improvements over in-context learning and prior fine-tuning baselines. The paper also includes ablations and an analysis of anonymization strategies.

Significance. If the disentanglement claim holds, G2S provides a practical recipe for improving LLM generalization to unseen TKG scenarios, which is timely given the growing interest in LLM-based temporal reasoning. The paper has several strengths: it evaluates across three distinct settings, includes multiple ablation variants, reports hyperparameter and infrastructure details, and provides a concrete case study illustrating a shared pattern across datasets. However, the central causal claim that anonymization in the general learning stage is what drives the zero-shot gains is not isolated by the experimental design. The general stage adds 130k training samples, and the ablations do not control for the effect of additional data or for the change in input format induced by anonymization. As a result, the current evidence supports the conclusion that the proposed framework works well, but not the stronger conclusion that disentanglement is the operative mechanism.

major comments (4)

[§5.1, §6.2, Table 2] The zero-shot comparison is confounded by the amount of training data. G2SGL(R)w.WIKI is fine-tuned on 130k general-stage samples (100k from GDELT and 30k from WIKI), whereas the ICL baselines (Llama2-ICL, Llama3-ICL, GPT-NeoX-ICL) and the Frequency baseline are not fine-tuned at all. The observed gains could therefore be explained by exposure to additional TKG-forecasting samples and task-format adaptation rather than by the anonymization-induced disentanglement. The paper should include a control trained on the same 130k samples with original entity/relation names and timestamps (i.e., without anonymization) in the general stage. Without such a control, the central claim that anonymization drives the improvement is not supported.
[§6.4, Table 4] The w/o GL ablation removes the entire general learning stage, but the remaining specific-stage ICL prompt still uses anonymized IDs with entity/relation mapping parts. This is not the same input format as the standard ICL baselines in Table 2 (Llama3-ICL reports H@1 31.9 on ICEWS14, while w/o GL reports 13.67). The large drop could be due to the model being unfamiliar with the ID-based format rather than losing access to general temporal patterns. A control that uses the same G2S prompt structure but with non-anonymized names in the general stage is needed to separate format adaptation from pattern learning.
[§6.2, Table 2] The result for G2SGL(F+Map) weakens the interference hypothesis. This variant deliberately injects scenario information during general learning, yet it matches or slightly exceeds G2SGL(F) on ICEWS14 (H@1 32.13 vs 31.52) and ICEWS18 (H@3 31.87 vs 31.17). The authors attribute this to the CAMEO schema overlap between GDELT and the ICEWS datasets, but that explanation suggests the improvements come from dataset-specific transfer rather than from learning invariant general patterns. The paper should explicitly reconcile this observation with the claim that simultaneously learning scenario information hurts generalization.
[§2, §5.3] zrLLM (Ding et al., 2024) is cited in the related work as an LLM-based method for zero-shot relational learning on TKGs, but it is not included among the baselines in Table 2. Since zrLLM is specifically designed for zero-shot TKG forecasting with LLMs, it is directly relevant to the zero-shot generalization claims. The authors should either add this comparison or justify its exclusion in the experimental section.

minor comments (3)

[§6.2] The description of G2SGL(R)w.WIKI states that it 'jointly utilizes GDELT and Y AGO' and adopts the RID strategy. Given the model name and the subsequent discussion about WIKI, this appears to be a typo: it should read 'GDELT and WIKI'.
[§6.2] The notation 'w. W IKI' contains an extraneous space; it should be 'w.WIKI' (or 'w/ WIKI') for consistency with the model name in Tables 2 and 4.
[Appendix A, Table 5] The statistics on multi-token IDs are useful, but the text could clarify whether the 'multi-token ID' percentages are computed on the validation set only and whether the same issue affects the test set used for the reported results.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: G2S is an empirical framework validated on held-out test sets, and its central claims do not reduce to their inputs by construction.

full rationale

The paper does not present a formal derivation chain; its claims are empirical and are evaluated on held-out test folds of ICEWS14, ICEWS18, and YAGO against non-LLM and LLM baselines. The general learning stage is trained on GDELT and WIKI data, while the reported zero-shot, low-resource, and standard results are genuine forecasts on datasets not used for general-stage training. The anonymization strategies, including the relative timestamp encoding and the FID/GID/RID entity and relation IDs, are operational choices rather than definitions that guarantee the outcome: the paper explicitly compares variants (e.g., G2SGL(F) vs. G2SGL(R) vs. G2SGL(R)w.WIKI) and reports mixed results, which gives the mechanism empirical content. The ablation w/o GL removes the entire general learning stage, so the improvement could partly reflect additional training data rather than anonymization alone; however, this is an experimental confound and a correctness risk, not a circularity in which a prediction equals its fitted input by construction. Validation-based selection of anonymization strategies is standard model selection, not fitting of test outcomes. Self-citations to earlier work by the same authors (e.g., RE-GCN and CluSTeR) appear only as baseline references and are not load-bearing for the framework's central claim, and no uniqueness theorem or self-cited theoretical guarantee is invoked to force the design. Overall, the paper is self-contained against external benchmarks and its central assertions stand or fall on the held-out experimental evidence.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new entities are postulated. The framework introduces abstract IDs as a representation, not as entities in the world. The main free choices are the history length, the general-stage sample sizes, and the per-dataset anonymization strategy; the latter is selected on validation data. The axioms are domain assumptions about what information is preserved by anonymization.

free parameters (3)

Historical context length L = 50
Set to 50 in all experiments; determines how many recent one-hop facts are included in each prompt (Section 4.1.1).
General-stage training sample sizes = 100,000 from GDELT; 30,000 from WIKI
Sampled approximately proportional to total training facts (Section 5.1); not swept, and the gain from this extra data is not isolated from the anonymization effect.
Anonymization strategy selection = RID for general stage; GID for ICEWS14/18 specific; RID for YAGO specific
Chosen based on validation-set performance (Sections 5.5 and 6.5); the zero-shot setting says strategies are selected via validation set, which is a model-selection step on the evaluation target.

assumptions (4)

domain assumption One-hop historical facts are sufficient to capture the temporal context needed for forecasting.
Eq. (1) restricts history to facts sharing the subject (or object) of the query; the paper acknowledges in Limitations that multi-hop and other strategies are not explored.
ad hoc to paper Anonymization to abstract IDs preserves the generalizable temporal patterns while removing scenario information.
This is the core premise of the general learning stage (Section 4.1.2). If false, transfer gains could come from extra training data alone.
domain assumption LLMs can learn reusable structural patterns from ID-formatted prompts.
The entire framework depends on the model treating abstract IDs as tokens and learning frequency/recurrence patterns from them (Section 4.1.3).
domain assumption Relative timestamps (query time as 0) eliminate train/test timestamp bias.
Eq. (2) assumes that the gap from query time is the only useful temporal feature and removes absolute date information.

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Cite this review

Pith. "Pith review of G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models." pith.science (2026). https://pith.science/paper/GNAHJJWL

@misc{pith2026250600445,
  author       = {Pith},
  title        = {Pith review of: G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GNAHJJWL}},
  note         = {Machine review of arXiv:2506.00445}
}

read the original abstract

Forecasting over Temporal Knowledge Graphs (TKGs) which predicts future facts based on historical ones has received much attention. Recent studies have introduced Large Language Models (LLMs) for this task to enhance the models' generalization abilities. However, these models perform forecasting via simultaneously learning two kinds of entangled knowledge in the TKG: (1) general patterns, i.e., invariant temporal structures shared across different scenarios; and (2) scenario information, i.e., factual knowledge engaged in specific scenario, such as entities and relations. As a result, the learning processes of these two kinds of knowledge may interfere with each other, which potentially impact the generalization abilities of the models. To enhance the generalization ability of LLMs on this task, in this paper, we propose a General-to-Specific learning framework (G2S) that disentangles the learning processes of the above two kinds of knowledge. In the general learning stage, we mask the scenario information in different TKGs and convert it into anonymous temporal structures. After training on these structures, the model is able to capture the general patterns across different TKGs. In the specific learning stage, we inject the scenario information into the structures via either in-context learning or fine-tuning modes. Experimental results show that G2S effectively improves the generalization abilities of LLMs.

Figures

Figures reproduced from arXiv: 2506.00445 by the authors.

**Figure 2.** The proposed G2S learning framework. Here, “ATS”, “SFT” and “ICL” denote the anonymous temporal [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** An example of the prompt together with an [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: The corresponding entity and relation ID map [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

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Pith tools

Reviewed August 7, 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...

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AI@Meta. 2024. https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md Llama 3 model card

2024

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