Papers by Yajing Yang
DataTales: A Benchmark for Real-World Intelligent Data Narration (2024.emnlp-main)
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| Challenge: | Existing benchmarks fail to capture the requisite analytical complexity for practical applications. |
| Approach: | They propose a benchmark to assess the proficiency of language models in data narration. |
| Outcome: | The proposed model combines financial reports with market data to demonstrate proficiency in data narration. |
KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration (2025.findings-emnlp)
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| Challenge: | KAHAN leverages LLMs as domain experts to drive the analysis. |
| Approach: | They propose a knowledge-augmented hierarchical framework that extracts insights from raw tabular data. |
| Outcome: | KAHAN outperforms existing frameworks on financial reporting benchmarks on narrative quality and factuality. |
Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach (2024.findings-acl)
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| Challenge: | Large Language Models generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt. |
| Approach: | They propose to refine a Large Language Model (LLM) with prompt-output pairs with equivalent semantics to achieve semantic consistency. |
| Outcome: | The proposed method improves the semantic consistency and task performance of LLMs. |
Intent Discovery with Frame-guided Semantic Regularization and Augmentation (2023.findings-acl)
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| Challenge: | Existing intent discovery methods focus on transferring prior knowledge of known intents to unknown ones. |
| Approach: | They propose to use frame knowledge as conceptual semantic guidance to bridge the gap between known intents representation learning and unknown intents clustering. |
| Outcome: | The proposed method outperforms solid baselines on two benchmark datasets. |