Papers with quick adaptation
MATILDA - Multi-AnnoTator multi-language InteractiveLight-weight Dialogue Annotator (2021.eacl-demos)
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| Challenge: | MATILDA is the first multi-annotator, multi-language dialogue annotation tool . it allows the creation of corpora, the management of users, the annotation of dialogues, the quick adaptation of the user interface to any language and the resolution of interannotation disagreement. |
| Approach: | They propose to use MATILDA to create corpora, manage users, and annotation dialogues. |
| Outcome: | The proposed tool supports the full pipeline for dialogue annotation, and non-technical people can use it. |
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs (2024.naacl-long)
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| Challenge: | Flow-adhering planning algorithm for task oriented dialogs (TODs) is a task-oriented dialog (TO) that can be used for task planning and API usage. |
| Approach: | They propose a Flow-Adhering Planning algorithm that follows predefined flows and preserves API dependencies in task oriented dialogs. |
| Outcome: | The proposed algorithm outperforms other decoding and prompting-based baselines in task oriented dialogs. |
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications (2023.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) are rapidly becoming more and more popular, but dealing with the potential harms associated with their deployment in real-world scenarios is still an open research question. |
| Approach: | They propose an automated approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications. |
| Outcome: | AART generates evaluation datasets with high diversity of content characteristics critical for effective adversarial testing. |
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data (2023.findings-emnlp)
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| Challenge: | Chain-of-thought (CoT) prompting is a new approach to prompt large language models (LLMs) but most studies rely on human-annotated rational chains to prompt LLMs . |
| Approach: | They propose a method that augments rational chains from a small labeled dataset and pruning low-quality chains to construct a pool of machine generated rationale chains based on the labels. |
| Outcome: | The proposed method can bypass human engineering of CoT by automatically augmenting rational chains from a small labeled dataset, and pruning low-quality chains to construct a candidate pool of machine generated rationale chains based on the labels. |
Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges (2024.lrec-main)
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| Challenge: | Existing self-supervised learning models can learn latent representations from large amounts of unlabeled data, but they are expensive to fine-tune. |
| Approach: | They develop a meta-adapter to obtain meta-initialized parameters for self-supervised models . meta-Adapters show better generalization and extensibility than traditional pretraining methods . |
| Outcome: | Experiments on common voice and FLEURS datasets show Meta-Adapter performs better on low-resource languages . authors show it can be used on 12 low-source languages, but it requires huge computational resources . |