Free your mouse! Command Large Language Models to Generate Code to Format Word Documents (2024.emnlp-main)
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| Challenge: | Recent LLMs have significantly improved code generation, making it increasingly accessible to users. |
| Approach: | They propose an automatic document formatting method, Text-to-Format, driven by various prompting strategies and a high-quality dataset DocFormEval data. |
| Outcome: | The proposed method improves the efficiency and experience of users in formatting the document and improves document formatting task. |
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| Challenge: | Existing approaches focus on training, fine-tuning or prompting LLMs to generate better outputs given the same input. |
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Making Large Language Models Better Data Creators (2023.emnlp-main)
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| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
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A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction (2024.findings-emnlp)
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| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
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LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
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CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases. |
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