Papers by Pheng-Ann Heng
Unveiling the Generalization Power of Fine-Tuned Large Language Models (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, but the comprehensive effects of fine-tuning on the LLMs’ generalization ability are not fully understood. |
| Approach: | They conduct extensive experiments across five distinct language tasks on different datasets to investigate whether fine-tuning affects the generalization ability intrinsic to LLMs. |
| Outcome: | The proposed model can generalize to different domains and tasks by integrating the in-context learning strategy during fine-tuning on generation tasks. |
SciVerse: Unveiling the Knowledge Comprehension and Visual Reasoning of LMMs on Multi-modal Scientific Problems (2025.findings-acl)
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| Challenge: | SciVerse is a multi-modal scientific evaluation benchmark to assess large multi-models . it examines the scientific knowledge comprehension, multi-mod content interpretation and Chain-of-Thought reasoning . authors examine the scientific proficiency of LMMs in scientific domains based on their work . |
| Approach: | They propose a multi-modal scientific evaluation benchmark to thoroughly assess Large Multi-modal Models across 5,735 test instances in five different versions. |
| Outcome: | The proposed evaluation reveals critical limitations in LMMs' scientific proficiency and provides new insights into future developments. |
LoRAExit: Empowering Dynamic Modulation of LLMs in Resource-limited Settings using Low-rank Adapters (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have exhibited remarkable performance across various natural language processing tasks, but deployment on resource-limited settings remains a challenge. |
| Approach: | They propose a dynamic inference architecture that leverages low-rank adaptors for efficient deployment of LLMs. |
| Outcome: | The proposed architecture significantly improves performance when deployed on resource-limited settings. |