Papers by Alex Kim
LLMs Behind the Scenes: Enabling Narrative Scene Illustration (2025.emnlp-main)
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| Challenge: | Generative AI has established the ability to readily transform content from one medium to another. |
| Approach: | They propose a pipeline that uses large language models to prompt text-to-image models to generate scenes for story text. |
| Outcome: | The proposed pipeline synthesizes illustrations for scenes in a story corpus using human annotation tasks. |
Towards Robust Mathematical Reasoning (2025.emnlp-main)
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Thang Luong, Dawsen Hwang, Hoang H Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu Hoang Trinh, Quoc V Le, Junehyuk Jung
| Challenge: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the international mathematical Olympiad level. |
| Approach: | They propose IMO-Bench, a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
| Outcome: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
Large Language Models as Realistic Microservice Trace Generators (2025.emnlp-main)
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| Challenge: | Obtaining real-world traces is difficult due to limited public data availability and the difficulty of collecting them at large scale from diverse environments. |
| Approach: | They propose to train a large language model to generate microservice call graphs using a recursive approach to capture hierarchical structures and implicit constraints in such traces. |
| Outcome: | The proposed method outperforms existing methods in accuracy and validity. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
DEBATE: Devil’s Advocate-Based Assessment and Text Evaluation (2024.findings-acl)
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| Challenge: | Existing methods for evaluating the quality of machine-generated texts have a relatively low correlation with human performance. |
| Approach: | They propose an NLG evaluation framework based on multi-agent scoring system augmented with a concept of Devil’s Advocate. |
| Outcome: | The proposed evaluation framework outperforms the previous state-of-the-art methods in two meta-evaluation benchmarks in NLG evaluation, SummEval and TopicalChat. |
HARE: an entity and relation centric evaluation framework for histopathology reports (2025.findings-emnlp)
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| Challenge: | evaluating the clinical quality of medical domain automated text generation remains a challenge. |
| Approach: | They propose a framework for histopathology automated report evaluation that prioritizes clinically relevant content by aligning critical histo pathology entities and relations between reference and generated reports. |
| Outcome: | The proposed framework outperforms existing metrics in histopathology report evaluations. |
Reconstruction Probing (2023.findings-acl)
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| Challenge: | a new analysis method for contextualized representations is proposed . contextualization boosts reconstructability of tokens close to the token being reconstructed . |
| Approach: | They propose a method for contextualized representations based on reconstruction probabilities in masked language models. |
| Outcome: | The proposed method compares reconstruction probabilities of tokens in masked language models . it finds that contextualization boosts reconstructability of token that are close to the token being reconstructed . |
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling (2024.acl-long)
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Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex Pentland, Yoon Kim, Deb Roy, Jad Kabbara
| Challenge: | a novel application of large language models (LLMs) to legal education helps non-experts learn complex legal concepts . authors find storytelling helps nonexperts understand complex legal terms and concepts compared to definitions . |
| Approach: | They propose a novel application of large language models to legal education . they use LLMs to generate legal stories explaining complex legal concepts . |
| Outcome: | The proposed method improves comprehension and interest among non-native speakers compared to definitions . the novel method also shows that non-experts retain more stories . |