Papers with instruction-tuning methods
Knowledge-augmented Financial Market Analysis and Report Generation (2024.emnlp-industry)
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| Challenge: | Existing methods to generate financial market analysis text require extensive financial knowledge and skill of financial analysts. |
| Approach: | They propose a task to generate financial market analysis reports using financial market data using a financial knowledge graph. |
| Outcome: | The proposed framework outperforms large-scale language models and retrieval-augmented baselines in the financial market analysis generation task. |
Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback (2024.emnlp-main)
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| Challenge: | Existing datasets do not cover full range of chart types, such as 3D, volumetric, and gridded charts. |
| Approach: | They propose a hierarchical pipeline and a new dataset for chart generation that leverages the relationships within rich datasets. |
| Outcome: | The proposed method outperforms open-source models and is comparable to state-of-the-art proprietary models in data visualization tasks. |
RUIE: Retrieval-based Unified Information Extraction using Large Language Model (2025.coling-main)
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| Challenge: | Unified information extraction (UIE) aims to extract diverse structured information from unstructured text using a single model or framework. |
| Approach: | They propose a framework that leverages in-context learning for efficient task generalization by combining LLM preferences with a keyword-enhanced reward model. |
| Outcome: | The proposed framework performs better on eight held-out datasets than existing methods and instruction-tuning methods. |
RECOST: External Knowledge Guided Data-efficient Instruction Tuning (2024.findings-acl)
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| Challenge: | Considering the high computing power overhead, data-efficient instruction tuning is proposed to reduce the training data size. |
| Approach: | They propose a framework to improve instruction tuning by integrating external knowledge into a single pipeline. |
| Outcome: | The proposed method achieves better results with only 1% of the full dataset. |
Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT - A Text-to-SQL Parsing Comparison (2023.findings-emnlp)
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| Challenge: | a number of open-source large language models claim to be performing better than commercial ones . however, these models fall short of the performance achieved by closed-source models like GPT-3.5 . |
| Approach: | They evaluate six popular large language models against each other to evaluate their performance . authors say open-source models are not as effective as those built by commercial models . |
| Outcome: | a new set of models claim to match or surpass the language understanding abilities of commercial models . the results show that the models performed far below the performance of closed-source models compared to open-source ones . |
Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents (2025.emnlp-main)
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| Challenge: | Large Language Models have demonstrated strong capabilities in transforming text descriptions or tables to data visualizations . however, it is not straightforward to apply these methods directly for a more real-world use case of visualizing data from long documents . |
| Approach: | They propose an unsupervised method for generating intent-based charts from documents . they propose an attribution-based metric that uses a structured textual representation of charts . |
| Outcome: | The proposed method outperforms baselines in terms of chart data accuracy and chart type over baselines. |