Papers by Xinmei Tian
Interpretable Composition Attribution Enhancement for Visio-linguistic Compositional Understanding (2024.emnlp-main)
Copied to clipboard
| Challenge: | Despite promising progress, vision-language models still exhibit significant challenges in understanding visio-linguistic concepts beyond object terms. |
| Approach: | They propose a framework that encourages the model to pay greater attention to composition words denoting relationships and attributes within the text. |
| Outcome: | The proposed framework improves the ability to discern intricate details and construct more sophisticated interpretations of combined visual and linguistic elements. |
Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. |
| Approach: | They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them. |
| Outcome: | The proposed method reduces hallucinations by reducing false activation and enhancing correct ones. |
Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality (2026.acl-long)
Copied to clipboard
| Challenge: | a contrastive learning approach for vision-language models is needed to capture compositional information. |
| Approach: | They propose a framework that masks compositional concepts in one modality and reconstructs them conditioned on full contextual information from the other . |
| Outcome: | The proposed framework enhances compositionality in visual language models and improves their ability to capture syntactic structure and linguistic information. |
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
| Approach: | They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries. |
| Outcome: | The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries . |