Papers by Victor Gutierrez Basulto
UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasing (2024.naacl-long)
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| Challenge: | Existing studies have investigated the potential of language models as knowledge bases and the existence of severe biases when extracting factual knowledge. |
| Approach: | They propose an adapter-based framework for generalised factual knowledge extraction using simple methods without introducing extra parameters. |
| Outcome: | The proposed framework improves the model’s out-of-domain generalisation and consistency under various prompts. |
Evaluating and Improving Graph to Text Generation with Large Language Models (2025.naacl-long)
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| Challenge: | Recent advances in large language models have revolutionized natural language processing due to their zero-and-short-shot capabilities. |
| Approach: | They propose a tuning-free prompting approach for graph-to-text generation tasks. |
| Outcome: | The proposed approach improves LLMs on graph-to-text generation tasks incrementally. |
MiCEval: Unveiling Multimodal Chain of Thought’s Quality via Image Description and Reasoning Steps (2025.naacl-long)
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Xiongtao Zhou, Jie He, Lanyu Chen, Jingyu Li, Haojing Chen, Victor Gutierrez Basulto, Jeff Z. Pan, Hanjie Chen
| Challenge: | Existing methods for evaluating the quality of reasoning steps in multimodal chain-of-thought are lacking. |
| Approach: | They propose a framework to evaluate the correctness of reasoning chains by evaluating the quality of both the description and each reasoning step. |
| Outcome: | The proposed framework improves interpretability and human judgments on four state-of-the-art MLLMs. |
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models (2024.findings-acl)
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| Challenge: | Multimodal Large Language Models fine-tuned with multimodal instruction-following data have demonstrated formidable capabilities in multimodal tasks. |
| Approach: | They propose to employ four PEFT methods to fine-tune the LLM component of open-source MLLMs. |
| Outcome: | The proposed method is the best performing on seven datasets, while fine-tuning the connector layers leads to improved performance in most MLLMs. |