Papers by Tiep Le
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning (2024.naacl-long)
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| Challenge: | Existing prompting methods rely on only one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. |
| Approach: | They propose a semi-structured prompting approach that integrates parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. |
| Outcome: | The proposed prompting method surpasses existing prompting methods even exceeding those that require fine-tuning on open-domain multi-hop question answering datasets. |
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression (2025.findings-naacl)
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Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu, Phillip Howard, Tiep Le, Sharath Nittur Sridhar, David Cobbley, Hao Kang, Vasudev Lal
| Challenge: | LVLMs have been shown to perform well on simple uni-modal benchmarks, but their detailed study on multi-modal models is still lacking. |
| Approach: | They propose a framework to analyze the impact of compression on LVLMs on multi-modal input driven tasks. |
| Outcome: | The proposed framework analyzes the impact of compression on generative performance of large vision language models on multi-modal input driven tasks. |