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The paper introduces ConFIT, a contrastive learning framework for financial text extraction that programmatically generates hard negatives using a Semantic-Preserving Perturbation (SPP) engine. SPP applies three strategies—entity swaps guided by Loughran–McDonald and Wikidata, numerical sensitivity adjustments, and context reordering—followed by two-stage filtering: perplexity-based quality control and NLI-based semantic proximity checks (Sections 4.1–4.2). The model is trained with a contrastive objective (Eq. 4) to distinguish clean vs. perturbed statements and is evaluated on FiQA (aspect-based sentiment) and SENTiVENT (event extraction) using FinBERT and Llama-3 8B (Sections 5.1–5.2). Table 1 reports sizable improvements over supervised fine-tuning, SimCSE, and GPT-4 zero-shot, with p-values reported as <0.001. Ablation studies (Table 2) attribute gains primarily to NLI and perplexity filtering and domain knowledge integration. The paper also analyzes a catastrophic failure in a multi-dataset synthetic setup (validation F1=0.000) and proposes mitigations (Section 5.8).
Cross‑Modal Consistency: 20/50
Textual Logical Soundness: 14/30
Visual Aesthetics & Clarity: 14/20
Overall Score: 48/100
Detailed Evaluation (≤500 words):
Image‑first understanding (visual ground truth)
• Figure 1/(a) F1 vs Epochs (Baseline): multi‑line plot; both Train/Val quickly reach ≈1.0.
• Figure 1/(b) Loss vs Epochs (Baseline): Train/Val losses monotonically decrease to ≈0.
• Figure 1/(c) Final Val F1 (Baseline): bars at exactly 1.000 for 10/15/20 epochs.
• Figure 2/(a) F1 Single vs Multi Synthetic: Single Train/Val →1.0; Multi Val ≈0 with spikes; Multi Train noisy 0.5–0.8.
• Figure 2/(b) Loss Single vs Multi Synthetic: Single →0; Multi Train ≈1.1–1.7; Multi Val ≈1.4–1.9 increasing.
• Figure 3/(a) Combined Final Performance: all setups 1.000 Train/Val except Synthetic‑Multi (Train 0.611, Val 0.000).
• Figure 4/(a) F1 Domain‑Specific: Single climbs to 1.0 by epoch ≈3; Multi flat at 1.0.
• Figure 4/(b) Loss Domain‑Specific: all losses quickly →0.
• Figure 4/(c) Final Performance Domain‑Specific: all bars 1.000.
1. Cross‑Modal Consistency
• Major 1: Overfitting claim conflicts with visuals. Evidence: Fig. 1(b) shows validation loss strictly decreasing to 0; text says “validation loss … then rises” (Sec. 6).
• Major 2: Figure‑2 narrative contradicts plots. Evidence: Text states “both configurations achieve high F1 … multi‑dataset … greater stability” (Sec. 6), but Fig. 2(a) shows Multi‑Val ≈0 and highly unstable.
• Major 3: Table‑level vs figure‑level metrics inconsistent. Evidence: Table 1 reports FiQA/SENTiVENT F1 ≈0.80, yet Fig. 3 shows Baseline/Synthetic‑Single/Domain‑Specific all Val F1=1.000.
• Minor 1: Captioning refers to “Figure 1: (Left)…(Middle)…,” but visuals are three separate panels without (a)/(b)/(c) labels.
• Minor 2: Figures don’t state dataset/model; readers cannot map them to FiQA/SENTiVENT without text.
2. Text Logic
• Major 1: Notation inconsistency in loss. Evidence: Eq.(4) uses “sin(·,·) … is the cosine similarity function.”
• Major 2: Model specification likely incorrect. Evidence: “DeBERTa‑v3‑large … 1.5B parameters” (Sec. 5.3) contradicts standard sizes, undermining reproducibility.
• Minor 1: Inference latency vs end‑to‑end numbers unclear (FinBERT 12 ms plus 8+3+5 ms still <23 ms?).
• Minor 2: Claim “maintaining 95% of full fine‑tuning performance” for LoRA lacks comparative evidence.
3. Figure Quality
• Major 1: Many plots show perfect 1.000 Val F1 without uncertainty; hampers credibility and interpretability.
• Minor 1: Small fonts and dense legends are borderline; still legible.
• Minor 2: Axes lack dataset/task labels; Figure‑alone comprehension is limited.
Key strengths:
• Clear method structure (SPP + two‑stage filtering) and thorough ablations (Table 2).
• Useful failure analysis of Synthetic‑Multi; mitigation ideas are practical.
Key weaknesses:
• Severe figure–text mismatches on core training‑dynamics claims.
• Notation/model‑size errors reduce trust.
• Figures lack essential context (dataset/model), and many show unrealistic perfect scores without variance.
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This paper introduces ConFIT (Contrastive Financial Information Tuning), a novel framework designed to enhance the performance of language models on financial text extraction tasks. The core idea revolves around a knowledge-guided contrastive learning approach, leveraging a Semantic-Preserving Perturbation (SPP) engine to generate high-quality, challenging negative samples. The SPP engine employs three perturbation strategies: entity swaps using the Loughran-McDonald lexicon and Wikidata, numerical sensitivity adjustments, and context reordering. These generated negatives are then filtered using perplexity and Natural Language Inference (NLI) techniques to ensure quality. The framework is evaluated on two financial datasets, FiQA and SENTiVENT, using both FinBERT and Llama-3 8B as base models. The empirical results demonstrate that ConFIT outperforms standard fine-tuning and SimCSE baselines, suggesting the effectiveness of the proposed approach. The authors also provide a detailed analysis of failure modes and practical deployment considerations, which is valuable for real-world applications. The paper's contribution lies in its specific adaptation of contrastive learning for the financial domain, incorporating domain-specific knowledge and a systematic approach to negative sample generation. While the paper presents a promising approach, it also reveals several areas that require further investigation and refinement. The lack of comparison with other state-of-the-art methods, the limited analysis of the SPP engine's inner workings, and the need for more detailed explanations of certain methodological choices are all areas that could be improved. Despite these limitations, the paper offers a valuable contribution to the field of financial NLP by introducing a novel and effective approach to contrastive learning.
I find several aspects of this paper to be commendable. The core strength lies in the paper's focus on the financial domain, which is an area that often requires specialized approaches due to its unique terminology and nuances. The introduction of the Semantic-Preserving Perturbation (SPP) engine is a notable contribution, as it provides a structured way to generate hard negatives that are relevant to financial text. The use of domain-specific knowledge sources, such as the Loughran-McDonald lexicon and Wikidata, for entity swaps is a clever way to inject domain expertise into the contrastive learning process. Furthermore, the inclusion of numerical sensitivity adjustments and context reordering adds to the robustness of the negative sample generation. The paper's empirical results, demonstrating improvements over standard fine-tuning and SimCSE baselines on both FiQA and SENTiVENT datasets, provide solid evidence for the effectiveness of the proposed approach. The evaluation using both FinBERT and Llama-3 8B further strengthens the findings, showing that ConFIT is effective across different model architectures. I also appreciate the authors' effort to provide a detailed analysis of failure modes and practical deployment considerations. This is a valuable contribution, as it provides insights into the limitations of the approach and offers guidance for real-world applications. The inclusion of computational efficiency analysis, including training time, inference latency, and memory usage, is also a positive aspect, as it demonstrates the practical feasibility of the framework. Finally, the paper's clear presentation of the methodology and experimental setup makes it relatively easy to understand and reproduce, which is crucial for the advancement of the field.
Despite the strengths, I have identified several weaknesses that warrant careful consideration. First, the paper lacks a comprehensive comparison with other state-of-the-art methods in financial sentiment analysis and information extraction. While the paper compares against standard fine-tuning, SimCSE, and zero-shot GPT-4, it omits comparisons with more recent and competitive models specifically designed for financial NLP tasks. This omission makes it difficult to assess the true novelty and effectiveness of the proposed approach relative to the current landscape of financial NLP. Second, the paper's evaluation is limited to two datasets, FiQA and SENTiVENT. While these are established datasets, they may not fully capture the diversity and complexity of real-world financial data. Evaluating the framework on a wider range of datasets, including those with different types of financial text (e.g., earnings calls, news articles, social media posts), would provide a more robust assessment of its generalizability. Third, the paper does not provide sufficient detail on the specific implementation of the context reordering strategy within the SPP engine. While the concept is mentioned, the lack of implementation details makes it difficult to understand how this strategy is applied in practice and whether it introduces any unintended biases or noise into the negative samples. Fourth, the paper lacks a detailed analysis of the SPP engine's effectiveness. While the paper shows overall performance improvements, it does not provide a granular analysis of how each component of the SPP engine (entity swaps, numerical adjustments, context reordering) contributes to the final performance. This lack of analysis makes it difficult to understand the strengths and weaknesses of each perturbation strategy and how they interact with each other. Fifth, the paper does not include a detailed ablation study of the perplexity and NLI filtering thresholds. While the paper mentions the thresholds used, it does not provide an analysis of how varying these thresholds affects the quality of the generated negatives and the final model performance. This lack of analysis makes it difficult to understand the sensitivity of the framework to these hyperparameters. Sixth, the paper's use of a T5-based model for negative generation without a clear justification is a concern. The paper does not explain why T5 was chosen over other language models, such as Llama, and it does not provide any analysis of the impact of this choice on the quality of the generated negatives. Seventh, the paper does not provide sufficient details on how the numerical sensitivity adjustments are made. While the paper provides a formula for numerical perturbation, it does not explain how the sensitivity parameter ε is determined or how the perturbations are applied in different contexts. This lack of detail makes it difficult to understand the practical implementation of this strategy. Eighth, the paper's explanation of the NLI filtering process is not sufficiently detailed. While the paper mentions that the NLI model is used to ensure semantic coherence, it does not explain how the NLI model is trained or fine-tuned, nor does it provide details on the specific NLI criteria used to filter the negatives. Finally, the paper lacks a detailed analysis of the computational cost of the proposed approach, particularly in comparison to other methods. While the paper provides some information on training time and inference latency, it does not provide a comprehensive analysis of the computational resources required for each step of the framework. All of these weaknesses are supported by the paper's content and have a significant impact on the overall conclusions.
To address the identified weaknesses, I recommend several concrete improvements. First, the authors should include a more comprehensive comparison with state-of-the-art methods in financial sentiment analysis and information extraction. This should include more recent and competitive models specifically designed for financial NLP tasks. This would provide a more robust assessment of the proposed approach's novelty and effectiveness. Second, the authors should evaluate the framework on a wider range of financial datasets, including those with different types of financial text. This would provide a more robust assessment of the framework's generalizability. Third, the authors should provide more specific details on the implementation of the context reordering strategy within the SPP engine. This should include examples of how sentences are restructured and the criteria used to ensure that the reordering does not alter the core meaning of the sentence. Fourth, the authors should conduct a more detailed analysis of the SPP engine's effectiveness. This should include a granular analysis of how each component of the SPP engine (entity swaps, numerical adjustments, context reordering) contributes to the final performance. Fifth, the authors should conduct a detailed ablation study of the perplexity and NLI filtering thresholds. This should include an analysis of how varying these thresholds affects the quality of the generated negatives and the final model performance. Sixth, the authors should provide a clear justification for the choice of T5 as the language model for negative generation. This should include a comparison with other language models, such as Llama, and an analysis of the impact of this choice on the quality of the generated negatives. Seventh, the authors should provide more details on how the numerical sensitivity adjustments are made. This should include an explanation of how the sensitivity parameter ε is determined and how the perturbations are applied in different contexts. Eighth, the authors should provide a more detailed explanation of the NLI filtering process. This should include details on how the NLI model is trained or fine-tuned, the specific NLI criteria used to filter the negatives, and an analysis of the impact of the NLI filtering on the quality of the negative samples. Finally, the authors should provide a more detailed analysis of the computational cost of the proposed approach, particularly in comparison to other methods. This should include a breakdown of the computational resources required for each step of the framework. By addressing these points, the authors can significantly strengthen the paper and provide a more robust and comprehensive evaluation of the proposed framework.
I have several questions that arise from my analysis of the paper. First, regarding the context reordering strategy, I am curious about the specific algorithms or rules used to rearrange the sentence structures. What criteria are used to ensure that the reordering does not alter the core meaning of the sentence, and how are semantic relationships between words preserved? Second, concerning the numerical sensitivity adjustments, how is the sensitivity parameter ε determined, and how are the perturbations applied in different contexts? Are there specific rules or heuristics used to ensure that the perturbations are semantically meaningful and do not introduce noise? Third, regarding the NLI filtering process, how is the NLI model trained or fine-tuned, and what specific NLI criteria are used to filter the negatives? What is the impact of the NLI filtering on the quality of the negative samples, and how does it affect the final model performance? Fourth, regarding the choice of T5 for negative generation, what specific characteristics of T5 make it suitable for this task, and how does its performance compare to other language models, such as Llama? What is the impact of this choice on the quality of the generated negatives? Fifth, regarding the perplexity and NLI filtering thresholds, how were these thresholds determined, and what is the impact of varying these thresholds on the quality of the generated negatives and the final model performance? What is the sensitivity of the framework to these hyperparameters? Finally, regarding the computational cost, what is the computational overhead of the SPP engine, and how does it compare to other methods? What are the practical implications of the computational cost for real-world deployment, especially in high-frequency financial applications?