Papers by Jaehoon Choi
ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization (2026.acl-long)
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| Challenge: | Existing methods for evaluating factual consistency are primarily designed for short summaries of isolated code snippets. |
| Approach: | They propose a reference-free and fine-grained method for evaluating factual consistency in real-world code summaries. |
| Outcome: | The proposed method achieves highest correlation with human judgment among 13 baselines, improving 15-18% over the previous state-of-the-art. |
EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL (2026.findings-acl)
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| Challenge: | Existing RL methods assign query-level rewards to all clauses, treating correct and incorrect clauses equally. |
| Approach: | They propose a method which provides fine-grained supervision through clause-level rewards. |
| Outcome: | Experiments on widely-used Text-to-SQL benchmarks show that EXPO-SqL outperforms existing methods by fine-grained clause-level learning. |
Whose Voice, Whose Avatar? Gender Matching Bias in Multimodal AI Teammates (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models are increasingly deployed as social agents . yet their ability to integrate conflicting identity cues remains underexplored . |
| Approach: | They audit gender bias in MLLMs that pair synthetic voices with avatars of varying gender presentation and visual fidelity. |
| Outcome: | The findings show that multimodal fairness is not monolithic . they show that models may appear unbiased on one dimension while enforcing stereotypes on another . |
DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph (2025.acl-long)
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| Challenge: | Existing methods for Text-to-SQL show little improvement compared to random selections . Existing approaches rely on intrinsic capabilities of hyper-scaled LLMs, not useful demonstrations. |
| Approach: | They propose a novel approach to effectively retrieving demonstrations and generating SQL queries by linking a question and its database schema items. |
| Outcome: | The proposed method shows consistent improvements in performance and efficiency across hyper-scaled LLMs and small LLM. |
Visual Interference in Speech Evaluation: Cultural Asymmetry and Cross-Modal Bias in MLLMs (2026.findings-acl)
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| Challenge: | a new paradigm shifts the paradigm of speech processing from simple transcription to complex social reasoning. |
| Approach: | They construct a cross-modal dataset to examine cultural asymmetry in MLLMs . they find that ML models actively reproduce context-dependent sociolinguistic ideologies based on native audio . |
| Outcome: | The proposed model exhibits cultural asymmetry in anglophone and Korean contexts . the model reproduces sociolinguistic ideologies, consistent with Expectancy Violation Theory . |
CliniCAST: Benchmarking Acoustic Grounding and Text Dominance in Medical Triage (2026.findings-acl)
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| Challenge: | Recent Large Audio-Language Models (LALMs) integrate acoustic capabilities into reasoning, yet whether they reliably ground clinical judgments in audible evidence remains unproven. |
| Approach: | They propose a benchmark that disentangles clinically meaningful acoustic cues from lexical content and speaker demographics. |
| Outcome: | Evaluating 5,856 synthetic samples across 12 disease conditions, the proposed model exhibits fragile acoustic grounding and pronounced "text dominance" failure mode. |