SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains

Ashay Srivastava; Drishti Sharma; Jebish Purbey; Nikhil Manali; Ram Mohan Rao Kadiyala; Siddartha Pullakhandam; Siddhant Gupta

REVIEW 4 major objections 5 minor 14 references

SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Sequential fine-tuning lifts financial misinformation detection on the FIN-FACT benchmark.

desk verdict Plausible but under-powered evidence for sequential fine-tuning on FIN-FACT; worth a serious referee if the authors add variance estimates and specify the preprocessing. read the letter →

arxiv 2412.00549 v1 pith:LSCK6HXQ submitted 2024-11-30 cs.CL cs.CEcs.LGq-fin.CP

Jebish Purbey , Siddhant Gupta , Nikhil Manali , Siddartha Pullakhandam , Drishti Sharma , Ashay Srivastava , Ram Mohan Rao Kadiyala This is my paper · ORCID

classification cs.CLcs.CEcs.LGq-fin.CP

keywords financialmisinformationdetectionsequentialfine-tuningexplanationgenerationFIN-FACTQwen2.5LoRAclaimverificationROUGE

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 describes a system for detecting financial misinformation and generating explanatory justifications. Its central claim is that training a large language model in two stages—first to classify claims alone, then to classify and explain simultaneously—outperforms standard single-stage joint training. On the FIN-FACT benchmark, the two-stage model, named SeQwen, achieved a micro F1 of 0.8366 on the development set and 0.8283 on the test set, with explanation quality (ROUGE-1) of 0.7170 and 0.7253 respectively. The authors argue that staged training lets the model specialize in verification before learning to articulate reasons, benefiting both tasks. If correct, this offers a simple training-schedule change that improves interpretable financial misinformation detection without larger models.

What carries the argument

The mechanism is a two-stage fine-tuning schedule on a generative LLM, using LoRA adapters. Stage one trains the model to output only a label given a claim and justification; stage two continues training to output both a label and an explanation in a fixed format. The same prompt template, taken from FMD-LLaMA, is used with the explanation component added in the second stage. The paper hypothesizes that the first stage stabilizes the classification decision, and the second stage learns to articulate that decision, producing better joint performance than training both objectives from the start.

What would settle it

Train the same Qwen2.5 7B model under three conditions—sequential fine-tuning, 8-epoch single-phase joint training, and 5-epoch joint training—with the justification-field preprocessing removed, repeated across five random seeds. If SeQwen's test-set advantages (micro F1 0.8283 vs. 0.8165, ROUGE-1 0.7253 vs. 0.6337) shrink below the run-to-run noise or reverse in this setup, the claimed benefit of sequential learning would not be sustained.

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Extended reading notes

Core claim

The paper's core discovery claim is that sequential fine-tuning of Qwen2.5 7B, with 3 epochs of classification-only training followed by 5 epochs of joint classification-and-explanation training, yields higher micro F1 and higher ROUGE scores than single-phase joint fine-tuning for the same total of 8 epochs. On the test set, SeQwen raised micro F1 from 0.8165 to 0.8283 and ROUGE-1 from 0.6337 to 0.7253 over the 5-epoch single-phase model. The authors present this as evidence that staged, task-specific training can mitigate the tradeoff between predictive accuracy and explanation generation in domain-specific claim verification.

Load-bearing premise

The comparison assumes that the 453-sample development set can reliably rank models when differences are as small as 0.01 micro F1, and that the preprocessing step of appending some claims from the justification field affects all compared training conditions equally.

Editorial extensions

If this is right

SeQwen's schedule is directly transferable to other verification-plus-explanation tasks, such as fact-checking with citations, without changing the model architecture.
The reported results suggest that total training budget can be split to favor explanation quality (5 of 8 epochs) after a shorter classification-only warm-up.
Small 7B models fine-tuned with LoRA at 4-bit precision can come close to or exceed larger models on financial misinformation benchmarks, lowering hardware requirements.
The gap between joint-only and sequential training points to an objective-ordering effect: which task is learned first can matter even when the final objective is the same.

Reading between the lines

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

The ROUGE gains may reflect the model learning the exact output format (Prediction: ... Explanation: ...) more than better reasoning; a human evaluation or semantic-similarity metric would be needed to separate format alignment from explanation quality.
The sequential schedule resembles a curriculum where the easier task (3-way classification) precedes the harder generative task; this suggests testing gradual mixing of the two losses rather than a hard switch.
Because the dev set has only 453 samples and the reported gains are around 0.01 F1, the model ranking and the SeQwen advantage should be checked with multiple seeds and bootstrapped confidence intervals before relying on the exact numbers.
If the preprocessing step (appending claims from the justification field) is what actually drives the improvement, then sequential learning may be confounded with data augmentation; a controlled ablation would separate the two.

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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

Read it. The one thing to know: this is a shared-task system description with a single load-bearing empirical claim—two stages of fine-tuning (3 epochs classification-only, then 5 epochs joint classification+explanation) beat single-phase joint fine-tuning for Qwen2.5 7B on FIN-FACT. The dev and test results point the same way, and the ROUGE gains are large (14-20% relative). If you work on financial misinformation detection, this is a cheap trick worth trying.

What's genuinely new: the specific comparison, sequential vs single-phase joint training for this benchmark, is not in FMD-LLaMA, which they cite. The model selection across six LLMs is honest work, and the dev table includes a matched 8-epoch single-phase baseline. That's the right control. The limitations section is candid about 4-bit precision, ROUGE's limits, and potential overfitting.

Now the soft spots, in order of weight. The whole case rests on single unreplicated runs: no seeds, no error bars, no significance test. On the 453-sample dev set, the Micro F1 gap between SeQwen and the 8-epoch baseline is 0.0132, roughly one standard error. The test gap of 0.0118 is about the same relative to its sample size. So the F1 headline isn't established. The ROUGE gains are more substantial and internally consistent, but ROUGE on generated text is high-variance and they don't test it. The test table omits the 8-epoch baseline that appears in the dev table; the dev comparison is the fair one, so this isn't fatal, but it makes the test narrative incomplete. The preprocessing sentence at the end of Section 3—'we appended some claims from the justification field'—is underspecified. If that augmentation wasn't identical across conditions, the comparison is confounded. And there's no released code to check the pipeline.

None of this proves the claim false. The pattern is plausible and matches curriculum-learning results elsewhere. But as it stands, this is suggestive evidence, not a demonstrated effect. The authors seem aware of some of these limits themselves.

Who gets value: practitioners building financial fact-checking or explanation systems, and shared-task readers benchmarking Qwen2.5. It's not a methods paper. I'd send it to a serious referee, but with the expectation that the authors add variance estimates, specify the augmentation, and include the 8-epoch test baseline. Without those, I'd treat the F1 gain as noise and the ROUGE gain as promising but unproven.

Referee Report

4 major / 5 minor

Summary. The paper describes SeQwen, the authors' submission to the COLING 2025 Financial Misinformation Detection (FMD) challenge. The system uses Qwen2.5 7B fine-tuned with LoRA in a sequential learning scheme: first 3 epochs of classification-only fine-tuning, then 5 epochs of joint classification and explanation generation. The authors evaluate several open-source LLMs on a development split of FIN-FACT, select Qwen2.5 7B as the best classifier, and then compare SeQwen against single-phase joint fine-tuning baselines on both the development and test splits. They report that SeQwen outperforms single-phase training on all reported metrics, e.g., Micro F1 of 0.8366 and ROUGE-1 of 0.7170 on the dev set, and Micro F1 of 0.8283 and ROUGE-1 of 0.7253 on the test set. The central claim is that the two-stage training schedule improves both classification and explanation quality over one-stage joint fine-tuning.

Significance. If the claimed improvement is real, the result is practically relevant for multi-task fine-tuning of LLMs in financial claim verification, showing that a simple staged curriculum can mitigate the classification/explanation tradeoff observed in single-phase joint training. The paper's strengths include an empirical comparison on a shared challenge benchmark, a clear description of the prompting and LoRA setup, and an honest limitations section that acknowledges the absence of human evaluation and the constraints of 4-bit precision. However, the significance is currently limited by the lack of statistical rigor: all results come from single unreplicated runs, the development set is small (453 samples), and the test-set comparison omits a matched 8-epoch single-phase baseline. The underspecified preprocessing augmentation also clouds the attribution of the gains to sequential learning. These issues are fixable but must be addressed before the central claim can be considered established.

major comments (4)

[Section 4, Tables 3 and 4] The central comparison rests on single runs with no reported seeds, error bars, or significance tests. On the 453-sample dev set, the Micro F1 gap between SeQwen (0.8366) and the 8-epoch single-phase baseline (0.8234) is 0.0132, which is within one standard error of a proportion near 0.83; on the 1304-sample test set, the gap of 0.0118 against the 5-epoch baseline is similarly about one standard error. The ROUGE gains are larger, but ROUGE on generated text is known to be high-variance and was not tested for statistical significance. Without multiple seeds or a significance test, the observed improvements could be run-to-run noise.
[Table 4] The test-set table compares SeQwen only to the 5-epoch single-phase joint model, omitting the 8-epoch single-phase baseline that appears in Table 3. Since SeQwen uses 8 total epochs of training, the test-set result does not control for total training budget. The dev-set comparison in Table 3 does include the 8-epoch baseline and shows a smaller gap, but the test-set validation of the headline claim would be strengthened by reporting the 8-epoch single-phase test-set numbers.
[Section 3, last paragraph of Methodology] The preprocessing step described as 'we appended some claims from the justification field, during the fine-tuning phase' is critically underspecified. It is not stated which conditions received this augmentation, how many claims were appended, how they were selected, or whether the augmentation was applied identically to the classification-only, joint, and sequential training runs. If the augmentation is not applied uniformly across all compared conditions, the entire comparison in Tables 3 and 4 is confounded, and the reported gains cannot be attributed to sequential learning.
[Section 4, Tables 3 and 4] The paper reports 'Overall Score' but never defines how it is computed from Micro F1 and the ROUGE scores. Since the overall score is used in the narrative (e.g., the 7.1% improvement on the test set), the formula must be given, otherwise the reader cannot verify whether the relative improvements are driven primarily by the ROUGE components, which appear to have larger relative gains than Micro F1.

minor comments (5)

[Abstract and Introduction] The term 'Minsinformation' in the Introduction is a typo for 'Misinformation'; please correct it.
[Section 2] The dataset section states the test set has 1304 samples, but Table 1 only reports the train and dev distributions; it would be helpful to also report the test class distribution for completeness, as the confusion matrices in the appendix are only for the dev set.
[Figure 3] The two prompt templates in Figure 3 are visually dense and the difference between them is easy to miss; a short caption or annotation highlighting the structural difference (label-only versus label-and-explanation response format) would improve clarity.
[Table 2] The table lists 'Qwen2.5 32B' but the methodology section enumerates only five models (Qwen2.5, Llama3 8B, Mistral 7B, Phi3 medium 4K, and Gemma-2 9B); the 32B variant should be mentioned in the model list for consistency.
[Limitations] The limitations section is candid about computational constraints and the absence of human evaluation, but it does not mention the lack of multiple runs or significance testing; adding this would be an honest reflection of the evidence level.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the sequential-learning claim rests on an empirical comparison against a held-out test set, not on a derivation from fitted inputs or self-citations.

full rationale

This paper is an empirical system description for the COLING 2025 FMD challenge. The central claim is that sequential fine-tuning (SeQwen) improves classification and explanation metrics over single-phase fine-tuning on the FIN-FACT benchmark. That claim is supported by dev-set model selection (Table 2 and Table 3) and by test-set evaluation (Table 4). No equation defines a predicted quantity in terms of the same measured quantity; no fitted parameter is renamed as a prediction; and no load-bearing result is imported from the authors' own prior work. The dev set is used to select the model, which is standard practice, and the test set is external to that selection. The omissions noted by the reader—no error bars, no significance tests, the 8-epoch single-phase baseline absent from the test table, and the underspecified preprocessing step about appending claims from the justification field—are experimental-validity and reporting concerns, not circularity. The paper's own Limitations section acknowledges robustness and evaluation limitations without asserting that any result is true by construction. Therefore no specific circular step can be quoted, and the appropriate score is 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities. Its central claim rests on hand-set training hyperparameters, a small dev set, and an underspecified preprocessing step. The main load-bearing choices are the epoch schedule, which is the very technique under test, and the hidden augmentation amount.

free parameters (5)

Per-stage training epochs = 3 epochs classification, 5 epochs joint
Chosen by hand; the central claim depends on this balance, and no sensitivity analysis is provided.
LoRA rank and alpha = r=16, alpha=16
Standard LoRA hyperparameters selected without reported tuning; affect all compared models equally.
Learning rate = 2e-4
Fixed for all fine-tuning runs; not optimized per model or stage.
Batch size = 16
Fixed total batch size; no exploration reported.
Preprocessing augmentation amount = unspecified (some claims)
The paper states claims are appended from the justification field without specifying how many or which; this is a hidden free parameter that could affect results.

assumptions (3)

domain assumption The dev set of 453 samples is representative of the test distribution and large enough to reliably rank models.
Model selection on dev determines which approach is reported on test; if dev rankings are noisy, the central comparison is undermined.
domain assumption The justification field is available at inference time for both training and test samples.
The prompt includes {justification}; the task provides it, but the method's gains depend on its availability.
ad hoc to paper Appending claim text from the justification field during training is a valid augmentation and does not introduce leakage or unfair advantage.
This preprocessing is unique to this paper and is described only as 'some claims'; its effect on the comparison is untested and could be a confound.

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Cite this review

Pith. "Pith review of SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains." pith.science (2026). https://pith.science/paper/LSCK6HXQ

@misc{pith2026241200549,
  author       = {Pith},
  title        = {Pith review of: SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LSCK6HXQ}},
  note         = {Machine review of arXiv:2412.00549}
}

read the original abstract

This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leveraged pre-processing and sequential learning for not only identifying fraudulent financial content but also generating coherent, and concise explanations that clarify the rationale behind the classifications. Our approach achieved competitive results with an F1-score of 0.8283 for classification, and ROUGE-1 of 0.7253 for explanations. This work highlights the transformative potential of LLMs in financial applications, offering insights into their capabilities for combating misinformation and enhancing transparency while identifying areas for future improvement in robustness and domain adaptation.

Figures

Figures reproduced from arXiv: 2412.00549 by the authors.

**Figure 2.** Distribution of financial claims across differ [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Comparison of prompts used for classification and classification & explanation generation. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Llama3 8B’s Confusion Matrix for classifica [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Mistral 7B’s Confusion Matrix for classifica [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 9.** Figure 9: Gemma-2 9B’s Confusion Matrix for classifi [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]

**Figure 7.** Figure 7: Qwen2.5 32B’s Confusion Matrix for classifi [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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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...

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" 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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