Papers by Seonghyeon Lee
OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification (2021.acl-long)
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| Challenge: | Recent studies on neural networks with pre-trained weights focus on low-dimensional subspace where the embedding vectors computed from input words are located. |
| Approach: | They propose an approach to find and regularize the remainder of the space, referred to as out-of-manifold, which cannot be accessed through the words. |
| Outcome: | The proposed approach is able to fine-tune the out-of-manifold embedding space on text classification benchmarks. |
Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) demonstrate remarkable reasoning capabilities comparable to human cognitive abilities. |
| Approach: | They propose a sentence-level rationale reduction framework that leverages likelihood-based criteria to selectively remove redundant reasoning sentences while preserving reasoning capabilities. |
| Outcome: | The proposed framework improves performance by 7.71% while reducing token generation by 19.87% compared to models trained with complete reasoning paths. |
Exploring Language Model’s Code Generation Ability with Auxiliary Functions (2024.findings-naacl)
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| Challenge: | Auxiliary function is a useful component to improve language model’s code generation ability, but a systematic exploration of how they affect has yet to be done. |
| Approach: | They construct a human-crafted evaluation set which contains examples of two functions where one function assists the other to examine their ability in a multifaceted way. |
| Outcome: | The proposed model is underutilized to call the auxiliary function, suggesting future directions to enhance their implementation by eliciting the supplementary function call ability encoded in the models. |
Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks (2024.emnlp-main)
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| Challenge: | Experimental results show that instruction tuning improves zero-shot generalization across various tasks and improves performance of specific tasks. |
| Approach: | They propose a task selection method that leverages instruction information alone to identify relevant tasks and optimize instruction tuning for specific tasks. |
| Outcome: | The proposed method is significantly more efficient than traditional approaches, which require complex measurements of pairwise transferability between tasks or the creation of data samples for the target task. |
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning (2022.acl-long)
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| Challenge: | Existing studies have shown that a pretrained language model can capture sentence similarity but there is no interpretation method for the sentence similarities. |
| Approach: | They propose a pretrained language model that captures sentence similarity between embeddings and a transport-based distance measure that leverages semantically-aligned token pairs. |
| Outcome: | The proposed framework outperforms baselines on both STS and interpretable-STS benchmarks and provides interpretation consistent with human judgement. |
Eliciting Instruction-tuned Code Language Models’ Capabilities to Utilize Auxiliary Function for Code Generation (2024.findings-emnlp)
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| Challenge: | Using auxiliary functions to implement functions is important for instruction-tuned models because it reduces the implementation difficulty of a target function compared to implementing them from scratch. |
| Approach: | They propose several ways to provide auxiliary functions to the models by adding them to the query or providing a response prefix to incorporate the ability to utilize auxiliary function with the instruction following capability. |
| Outcome: | The proposed models outperform the recent powerful language models, gpt-4o, in the code generation task. |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark (2024.lrec-main)
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| Challenge: | KoDialogBench is a benchmark designed to assess language models’ conversational capabilities in low-resource languages such as Korean. |
| Approach: | They propose a benchmark to assess language models’ conversational capabilities in Korean by collecting native Korean dialogues from public sources and translating them into diverse test datasets. |
| Outcome: | The proposed benchmark measures the conversational capabilities of language models in Korean, and shows that they can improve on previous training techniques. |
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for topic taxonomies focus on frequent terms and local topic-subtopic relations, which leads to limited topic term coverage. |
| Approach: | They propose a framework for topic taxonomy expansion that directly generates topic-related terms belonging to new topics. |
| Outcome: | The proposed framework outperforms baseline methods on two real-world text corpora. |
TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models (2022.emnlp-main)
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Joel Jang, Seonghyeon Ye, Changho Lee, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Minjoon Seo
| Challenge: | Language Models (LMs) become outdated as the world changes, a phenomenon called temporal misalignment. |
| Approach: | They propose a lifelong benchmark that utilizes the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation. |
| Outcome: | The proposed benchmark can be trained on the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation. |
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code (2025.findings-emnlp)
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| Challenge: | Language models (LMs) have exhibited impressive abilities in generating code from natural language requirements. |
| Approach: | They propose to introduce various metrics with inter-code similarity to evaluate the diversity of generated code by comparing model-generated solutions with human-written ones. |
| Outcome: | The proposed method leverages LMs’ capabilities in code understanding and reasoning, resulting in a set of metrics that represent the number of algorithms in model-generated solutions. |