Papers by Shengguang Wu
Symmetrical Visual Contrastive Optimization: Aligning Vision-Language Models with Minimal Contrastive Images (2025.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that Large Vision-Language Models (VLMs) tend to neglect image content and over-rely on language-model priors, resulting in errors in visually grounded tasks and hallucinations. |
| Approach: | They propose a novel finetuning objective that steers the model toward capturing important visual details and aligning them with corresponding text tokens. |
| Outcome: | The proposed method achieves up to 22% reduction in hallucinations and significant gains in vision-centric and general tasks while maintaining or improving the model's general abilities. |
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for visual storytelling suffer from low inference speed and are not well-suited for synthetic scenes. |
| Approach: | They propose a diffusion-based system that generates visual descriptions as a single conditional denoising process. |
| Outcome: | The proposed system improves inter-sentence coherence and image-to-text fidelity. |
Finetuning LLMs for Human Behavior Prediction in Social Science Experiments (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models can be used to simulate social science experiments . finetuning LLMs directly on individual-level responses from past experiments improves accuracy . |
| Approach: | They propose to fine tune large language models directly on individual responses from past experiments to achieve multiple levels of generalization. |
| Outcome: | The proposed model outperforms GPT-4o in completely unseen studies by 36% . the proposed model reduces demographic parity difference by 10.6% compared to GPT-4) |
Rethinking Pragmatics in Large Language Models: Towards Open-Ended Evaluation and Preference Tuning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to assess social-pragmatic inference in large language models are inadequacy, and preferential tuning is the best approach. |
| Approach: | They propose to use free-form models' responses as a measure to assess social-pragmatic reasoning and advocate for preference optimization over supervised finetuning (SFT). |
| Outcome: | The proposed model outperforms supervised finetuning (SFT) and offers a near-free launch in pragmatic abilities without compromising general capabilities. |