Papers by Jongwoo Kim
Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA (2026.eacl-long)
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| Challenge: | Recent studies leverage large language models (LLMs) in LVQA benchmarks, achieving exceptional performance while relying on vision language models to convert all visual content into natural language. |
| Approach: | They propose a modular and training-free framework that leverages large language models to generate a small subset of informative frames tailored to each question. |
| Outcome: | The proposed framework achieves state-of-the-art performance among similar models across four benchmark LVQA datasets: EgoSchema, NExT-QA, IntentQA, VideoMME. |
FeedEval: Pedagogically Aligned Evaluation of LLM-Generated Essay Feedback (2026.findings-acl)
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| Challenge: | Recent research emphasizes the generation of high-quality feedback that provides justification and actionable guidance. |
| Approach: | They propose an LLM-based framework for evaluating LLM feedback along three dimensions: specificity, helpfulness, and validity. |
| Outcome: | The proposed framework evaluates LLM-generated feedback along three dimensions: specificity, helpfulness, and validity. |
Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have strong reasoning and generalization abilities, but they struggle to reflect the histories of example learners within a single prompt during in-context learning. |
| Approach: | They propose a LLM-based option weighted knowledge tracing framework that encodes the interaction histories of example learners in context as textual categorical option weights. |
| Outcome: | The proposed framework outperforms existing LLM-based KT models in warm-start and few-shot settings. |
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models (2023.findings-emnlp)
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| Challenge: | Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures. |
| Approach: | They propose a framework that narrows the encoder and shortens the decoder networks of encoder-decoder models. |
| Outcome: | The proposed framework reduces the number of decoder layers and improves generation quality. |