Papers by Zae Myung Kim
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? (2021.findings-acl)
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| Challenge: | Recent studies on multilingual representations focus on whether there is an emergence of language-independent representations or whether multilingual models partition their weights among different languages. |
| Approach: | They analyze encoder self-attention and encoder-decoder attention heads in a multilingual neural translation model. |
| Outcome: | The proposed model is based on a multilingual neural translation model with a language-independent representation. |
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks (2022.emnlp-main)
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| Challenge: | Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness. |
| Approach: | They propose to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans with their corresponding edit intents. |
| Outcome: | The proposed system outperforms baselines on other text revision tasks and human evaluations. |
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. |
Understanding Iterative Revision from Human-Written Text (2022.acl-long)
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| Challenge: | This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. |
| Approach: | They propose to annotate iteratively revised text using a multi-domain annotated corpus that generalizes to a variety of domains, edit intentions, revision depths, and granularities. |
| Outcome: | The proposed model improves automatic evaluations by integrating edit intentions with writing quality. |
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs (2024.acl-long)
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| Challenge: | Empirical findings show that although both LLMs and humans generate distinct discourse patterns influenced by specific domains, human-written texts exhibit more structural variability, reflecting the nuanced nature of human writing in different domains. |
| Approach: | They propose a method to leverage hierarchical parse trees and recursive hypergraphs to uncover distinctive discourse patterns in texts written by humans and LLMs. |
| Outcome: | The proposed method combines hierarchical parse trees and recursive hypergraphs to uncover distinctive discourse patterns in texts produced by both LLMs and humans. |