Papers by Jaewoo Ahn
Who Wrote this Code? Watermarking for Code Generation (2024.acl-long)
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| Challenge: | Existing methods to detect machine-generated text by embedding watermarks fail to function appropriately in code generation tasks due to the task’s nature of having low entropy. |
| Approach: | They propose a logit-modifying watermark method which enhances detection ability and mitigates code quality degeneration by removing low-entropy segments at generating and detecting watermarks. |
| Outcome: | The proposed method outperforms baseline methods in detecting machine-generated code text while preserving code quality. |
FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games (2025.emnlp-main)
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| Challenge: | Existing game benchmarks lack diversity and evaluate GUI agents on completing entire storylines. |
| Approach: | They propose a benchmark of 34 Flash-based adventure games to test full story arc completion and tackle observation-behavior gap. |
| Outcome: | The proposed benchmarks show GUI agents struggle with full story arc completion while others improve on observation-behavior gaps. |
Is a Peeled Apple Still Red? Evaluating LLMs’ Ability for Conceptual Combination with Property Type (2025.naacl-long)
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| Challenge: | Conceptual combination is a cognitive process that merges basic concepts, enabling the creation of complex expressions. |
| Approach: | They propose to use a Conceptual Combination with Property Type dataset to evaluate LLMs for conceptual combination thoroughly. |
| Outcome: | The proposed method improves performance in all generative tasks. |
Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of Pre-trained Multimodal Representation via Text Updates (2025.acl-long)
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| Challenge: | Recent advances in multimodal systems have demonstrated remarkable capabilities in generating multimodal content from multimodal inputs. |
| Approach: | They propose a benchmark that leverages large language models to generate deceptive text samples to exploit compositional vulnerabilities across different modalities. |
| Outcome: | The proposed approach exploits compositional vulnerabilities across images, videos, and audios. |
MPCHAT: Towards Multimodal Persona-Grounded Conversation (2023.acl-long)
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| Challenge: | Existing research on persona-based dialogue has focused on textual persona that delivers personal facts or personalities, but image modality can reveal the speaker’s personal characteristics and experiences in episodic memory. |
| Approach: | They propose a multimodal persona-based dialogue dataset which extends persona with both text and images to contain episodic memories. |
| Outcome: | The proposed dataset extends persona with text and images to contain episodic memories. |
TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) can be used to simulate human behaviors, but point-in-time role-playing is a key component of fandom role-players. |
| Approach: | They propose a benchmark to evaluate point-in-time character hallucination in role-playing LLMs. |
| Outcome: | The proposed method reduces point-in-time character hallucinations effectively by decomposing reasoning steps and using narrative experts. |
mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images (2023.emnlp-main)
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| Challenge: | Existing summarization datasets do not cover multimodal discussions, multiple modalities, or both . mRedditSum consists of 3,033 discussion threads and images with human-written summaries. |
| Approach: | They propose a multimodal discussion summarization dataset that annotates 3,033 discussion threads with a human-written summary. |
| Outcome: | The proposed method outperforms existing models and serves as competitive baseline for future work. |