Papers by Ryan Koo
CoEdIT: Text Editing by Task-Specific Instruction Tuning (2023.findings-emnlp)
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| Challenge: | We present a large language model for writing assistance that is fine-tuned on task-specific instructions. |
| Approach: | They propose a large language model that is fine-tuned on task-specific instructions and outputs the edited text. |
| Outcome: | The proposed model performs better than other state-of-the-art models on various editing benchmarks while being 60x smaller. |
Benchmarking Cognitive Biases in Large Language Models as Evaluators (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have been shown to be effective as automatic evaluators with simple prompting and in-context learning. |
| Approach: | They assemble 16 Large Language Models and evaluate their outputs by preference ranking . they introduce a cognitive bias benchmark to measure six different cognitive biases in LLM evaluation outputs. |
| Outcome: | The proposed model is biased on the CoBBLer benchmark, indicating that machine preferences are misaligned with humans. |
Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation (2024.emnlp-main)
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| Challenge: | Prior work explored the domain of controlled style generation, a task in which a generative language model aims to generate text with a specified style 2 . however in practice, text often contains not only a single style, but a combination of styles. |
| Approach: | They propose to use calibrated outputs from discriminators and dynamic weighting by discriminator gradient magnitudes to combine multiple styles in a reward function. |
| Outcome: | The proposed dynamic weighting outperforms static weighting approaches with respect style control while maintaining linguistic quality. |