Papers by Kanishk Singh
Learning to Follow Object-Centric Image Editing Instructions Faithfully (2023.findings-emnlp)
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| Challenge: | avrahami et al., 2022b,a): natural language instructions are often underspecified, requiring models to uncover their implicit meaning. |
| Approach: | They propose to use paired data to model the implicit meaning of instructions . they also propose to ground the model to localize where the edit has to be performed . |
| Outcome: | The proposed model performs better than state-of-the-art baselines on paired data, showing improvements in quality and faithfulness. |
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings (2024.findings-emnlp)
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| Challenge: | Existing methods for text style transfer rely on few-shot capabilities of large language models or complex controllable text generation approaches that are inefficient and underperform on fluency metrics. |
| Approach: | They propose a lightweight but effective approach which leverages a small language model and pre-trained authorship embeddings to perform efficient, few-shot text style transfer. |
| Outcome: | The proposed method outperforms strong approaches such as GPT-4 and performs form attribute style transfer with automatic and human evaluations. |