Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument Analysis Article Swipe
Fei Ding
,
Xin Kang
,
Linhuang Wang
,
Yunong Wu
,
Satoshi Nakagawa
,
Fuji Ren
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13091746
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13091746
The field of argument analysis has become a crucial component in the advancement of natural language processing, which holds the potential to reveal unprecedented insights from complex data and enable more efficient, cost-effective solutions for enhancing human initiatives. Despite its importance, current technologies face significant challenges, including (1) low interpretability, (2) lack of precision and robustness, particularly in specialized fields like finance, and (3) the inability to deploy effectively on lightweight devices. To address these challenges, we introduce a framework uniquely designed to process and analyze massive volumes of argument data efficiently and accurately. This framework employs a text-to-text Transformer generation model as its backbone, utilizing multiple prompt engineering methods to fine-tune the model. These methods include Causal Inference from ChatGPT, which addresses the interpretability problem, and Prefix Instruction Fine-tuning as well as in-domain further pre-training, which tackle the issues of low robustness and accuracy. Ultimately, the proposed framework generates conditional outputs for specific tasks using different decoders, enabling deployment on consumer-grade devices. After conducting extensive experiments, our method achieves high accuracy, robustness, and interpretability across various tasks, including the highest F1 scores in the NTCIR-17 FinArg-1 tasks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13091746
- OA Status
- gold
- Cited By
- 2
- References
- 38
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4396568874Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/electronics13091746Digital Object Identifier
- Title
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Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument AnalysisWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-01Full publication date if available
- Authors
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Fei Ding, Xin Kang, Linhuang Wang, Yunong Wu, Satoshi Nakagawa, Fuji RenList of authors in order
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https://doi.org/10.3390/electronics13091746Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/electronics13091746Direct OA link when available
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Interpretability, Computer science, Robustness (evolution), Inference, Machine learning, Artificial intelligence, Causal inference, Data mining, Theoretical computer science, Data science, Biochemistry, Gene, Economics, Chemistry, EconometricsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3023384820, https://openalex.org/W2918821272, https://openalex.org/W3016693931, https://openalex.org/W4242601385, https://openalex.org/W3035669514, https://openalex.org/W4239510810, https://openalex.org/W6639619044, https://openalex.org/W2144499799, https://openalex.org/W2157331557, https://openalex.org/W4318069287, https://openalex.org/W2808610848, https://openalex.org/W4391583853, https://openalex.org/W4382998379, https://openalex.org/W2327805699, https://openalex.org/W2979666134, https://openalex.org/W6793656017, https://openalex.org/W4391074069, https://openalex.org/W4289840706, https://openalex.org/W4311978971, https://openalex.org/W4205812715, https://openalex.org/W3174245192, https://openalex.org/W4389352457, https://openalex.org/W4391760832, https://openalex.org/W4285137661, https://openalex.org/W6769627184, https://openalex.org/W3202729335, https://openalex.org/W3194005300, https://openalex.org/W3174432697, https://openalex.org/W6798057236, https://openalex.org/W3214556263, https://openalex.org/W6809646742, https://openalex.org/W2896457183, https://openalex.org/W4285294723, https://openalex.org/W4221143046, https://openalex.org/W4231510805, https://openalex.org/W4288089799, https://openalex.org/W3155407547, https://openalex.org/W3185341429 |
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