Papers by Yi R. Fung
MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing (2025.findings-emnlp)
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| Challenge: | Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited. |
| Approach: | a new benchmark is designed to diagnose reliability in text-guided medical image editing. a clinically grounded evaluation framework measures Editing Accuracy, Context Preservation, and Visual Quality. |
| Outcome: | a new benchmark is designed to diagnose reliability in medical image editing. |
Aligning LLMs with Individual Preferences via Interaction (2025.coling-main)
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| Challenge: | Existing studies on LLMs alignment focus on generalizing their behavior to generalized values such as helpfulness, harmlessness, and honesty. |
| Approach: | They train large language models to "interact to align" to implicitly infer user preferences . they use a multi-turn preference dataset to generate a personalized alignment . |
| Outcome: | The proposed method enables dynamic, personalized alignment via interaction with a multi-turn preference dataset. |
Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing approaches to online claim verification rely on prompt engineering or pre-designed reasoning workflows. |
| Approach: | They propose an online reinforcement learning framework that enables an LLM to interact with a search engine and receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors. |
| Outcome: | Empirical results show that Veri-R1 improves joint accuracy by 30% and doubles evidence score, often surpassing larger-scale model counterparts. |
MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration (2025.acl-long)
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| Challenge: | Existing methods calibrate model confidence on entire response, which leads to incorrect answers with high confidence. |
| Approach: | They propose a framework that advances the knowledge boundary awareness of multimodal large language models through reasoning step confidence calibration. |
| Outcome: | Empirical results show that the proposed framework outperforms existing methods across domains and metrics. |
EMCompress: Video-LLMs with Endomorphic Multimodal Compression (2026.findings-acl)
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| Challenge: | Static, sparse frame sampling either dilutes evidence across task-irrelevant segments at significant cost or misses fine-grained temporal semantics altogether. |
| Approach: | They propose a novel task that compresses multimodal input while preserving answer invariance across reasonable downstream models. |
| Outcome: | The proposed task surpasses prior methods by 0.40 F-1 with competitive query rewriting. |
Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward (2025.findings-acl)
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| Challenge: | Large Vision Language Models (LVLMs) have shown impressive performance on various vision-language tasks. |
| Approach: | They propose a benchmark framework for evaluating Visual Variation Robustness of Large Vision Language Models that incorporates automated evaluation dataset generation and principled metrics for thorough robustness assessment. |
| Outcome: | The proposed framework identifies a vulnerability to visual variations affecting even advanced models that excel at complex vision-language tasks but significantly underperform on simple tasks like object recognition. |
Let’s Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM’s Math Capability (2025.emnlp-main)
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| Challenge: | Recent work has focused on improving the mathematical reasoning capabilities of Large Language Models (LLMs). |
| Approach: | They propose an end-to-end framework to integrate FL into NL math reasoning . they propose a problem alignment method that reformulates QA and existence problems . |
| Outcome: | The proposed framework achieves 89.80% and 84.34% accuracy rates on the MATH-500 and the AMC benchmarks. |
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness (2025.emnlp-main)
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| Challenge: | Large language models produce non-existing facts when faced with questions outside their parametric knowledge, which undermines their reliability. |
| Approach: | They propose a method that separates the learning of answer prediction and confidence estimation during fine-tuning on instruction data. |
| Outcome: | Experiments on multiple models and different model sizes show that the proposed method outperforms baselines by up to 25% in average precision. |
A Zero-Shot Claim Detection Framework Using Question Answering (2022.coling-1)
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| Challenge: | Existing claims detection frameworks are portability to emerging events and low-resource training data settings. |
| Approach: | They propose a claim detection framework that leverages zero-shot Question Answering to solve sub-tasks such as topic filtering, claim object detection, and claimer detection. |
| Outcome: | The proposed framework outperforms baselines on the NewsClaims benchmark. |
Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable abilities, but they invariably generate flawed responses. |
| Approach: | They propose a self-correction approach that instructs VLMs to refine their outputs by allowing them to learn from their self-generated self-reference data without external feedback. |
| Outcome: | The proposed approach enables VLMs to learn from their self-generated self-correction data without relying on external feedback, facilitating self-improvement. |
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents (2026.acl-long)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) focus on task completion, but neglect a crucial capability: the ability to devise and adjust cost-optimal plans in response to changing environments. |
| Approach: | They propose a scalable, cost-centric benchmark to evaluate agents’ economic reasoning and replanning abilities. |
| Outcome: | Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning . |
CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have emerged as powerful assistants for scientific writing, but reliability of LLM alone is in doubt. |
| Approach: | They propose a retrieval-aware agent framework to provide more faithful grounding for citation validation. |
| Outcome: | The proposed framework improves over the baseline and achieves 68.1% accuracy on the CiteME benchmark, approaching human performance. |
VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues (2025.acl-long)
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| Challenge: | Existing vision-language models lack the ability to visually link matching visual cues across images or frames. |
| Approach: | They propose a benchmark to assess whether vision-language models can Visually Link Matching cues with 9 subtasks and over 3,000 test cases. |
| Outcome: | The proposed benchmarks on multiple images and videos do not demonstrate that vision-language models can link visual cues across images or frames. |
Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models have demonstrated remarkable mathematical problem-solving abilities, but their true reasoning shortcomings are often hidden. |
| Approach: | They propose to leverage the rigor and methodological complexity of mathematical proofs as a diagnostic tool to expose hidden failures. |
| Outcome: | The proposed model evaluation exploits the rigor and complexity of proof problems to uncover 10 fine-grained errors. |
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language Models (2025.findings-acl)
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| Challenge: | Existing pruning methods fail to account for unique token attributes across layers and modalities inherent to MLLMs. |
| Approach: | They propose a pruning framework that takes into account unique token attributes across layers and modalities inherent to MLLMs. |
| Outcome: | The proposed pruning framework outperforms existing pruning techniques on two state-of-the-art MLLMs. |
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are pre-trained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge. |
| Approach: | They propose to use the **C**ross-Lingual Self-**Aligning ability of **L**anguage **M**odels to align knowledge across languages. |
| Outcome: | The proposed model performs well in both zero-shot and retrieval-augmented settings. |
End-to-End Optimization for Multimodal Retrieval-Augmented Generation via Reward Backpropagation (2025.findings-emnlp)
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| Challenge: | MM-RAG is a promising approach for enhancing the reliability and factuality of large vision-language models . current methods focus on component-level optimizations and necessitate extensive component-specific training datasets . |
| Approach: | They propose a new paradigm that backpropagates global rewards to each component . this backpropage transforms local losses into specific local losses . |
| Outcome: | The proposed paradigm achieves high training efficiency on knowledge-intensive multimodal benchmarks. |
The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination (2025.findings-acl)
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Yuji Zhang, Sha Li, Cheng Qian, Jiateng Liu, Pengfei Yu, Chi Han, Yi R. Fung, Kathleen McKeown, ChengXiang Zhai, Manling Li, Heng Ji
| Challenge: | Hallucination is a persistent challenge in large language models where even with rigorous quality control, models often generate distorted facts. |
| Approach: | They propose a new framework to quantify factual hallucinations by modeling knowledge overshadowing. |
| Outcome: | The proposed framework improves model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%). |
Learning Diverse Responses with Prefix-Conditioned Supervised Fine-Tuning (2026.acl-long)
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| Challenge: | Large language models exhibit highly homogeneous, repetitive responses, resulting in inefficient exploration. |
| Approach: | They propose a method that constructs semantically consistent yet distributionally distinct prior contents to different responses and decouple the one-to-many mapping. |
| Outcome: | The proposed method improves absolute performance by 5.3% and increases generation diversity by 198.3% on average while significantly enhancing output diversity and test-time scaling. |