Papers by Ruining Zhao
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction (2023.emnlp-main)
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| Challenge: | Large language models with instruction-following capabilities are not suitable for long-tail ad hoc extraction use cases for non-expert users. |
| Approach: | They propose a task that follows instructions to extract the desired content from the associated text and present it in a structured tabular format. |
| Outcome: | The proposed paradigm outperforms existing open-source models of similar size in terms of information extraction. |
RESTful-Llama: Connecting User Queries to RESTful APIs (2024.emnlp-industry)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated exceptional performance in zero-shot learning and reasoning tasks. |
| Approach: | They propose a framework that transforms natural language instructions into effective RESTful API calls and a method to generate fine-tuning datasets from public API documentation. |
| Outcome: | The proposed framework improves performance in a 31.9% improvement in robustness and 2.33x increase in efficiency compared to existing methods. |
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification (2023.acl-long)
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| Challenge: | Recent methods for event schema induction use information extraction systems to construct event graph instances from documents . compared to the previous state-of-the-art closed-domain schema inducing model, human assessors were able to cover 10% more events when translating the schemas into coherent stories . |
| Approach: | They propose to treat event schemas as commonsense knowledge that can be derived from large language models. |
| Outcome: | The proposed method simplifies the schema induction process and improves readability. |