MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show zero-shot performance on unseen multimodal tasks . instruction tuning has yet to be explored for vision and multimodal task. |
| Approach: | They propose a multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories. |
| Outcome: | The proposed model performs well on unseen multimodal tasks and is highly scalable. |
Similar Papers
Deep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on instruction tuning to show cross-lingual generalization . a novel non-English meta-dataset is used to study instruction tuning . |
| Approach: | They perform instruction tuning individually for two distinct language meta-datasets and assess the performance on unseen tasks in a non-English language. |
| Outcome: | The proposed model outperforms baseline training in English and Korean by 20.7% and 13.6%. |
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization (2024.naacl-long)
Copied to clipboard
Dongsheng Zhu, Daniel Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, Dawei Yin
| Challenge: | Current MMLMs show impressive zero-shot abilities in multi-modal tasks, but their performance depends heavily on the quality of instructions. |
| Approach: | They propose a novel approach to advancing multi-modal language models in zero-shot learning by evaluating and optimizing instructional texts through In-Context Learning. |
| Outcome: | The proposed approach improves zero-shot performance in multi-modal tasks by evaluating and optimizing instructional texts. |
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)
Copied to clipboard
Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains. |
| Approach: | They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs. |
| Outcome: | The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods. |
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Instruction tuning is emerging in NLP, but has not been explored for dialogue-related tasks. |
| Approach: | They propose an instruction tuning framework for dialogue that leverages natural language instructions with language models to induce zero-shot generalization on unseen tasks. |
| Outcome: | The proposed framework enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection. |
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models (2022.acl-long)
Copied to clipboard
| Challenge: | Massively Multilingual Transformer based Language Models have been shown to be effective on zero-shot transfer across languages, though performance varies from language to language depending on pivot language(s) used for fine-tuning. |
| Approach: | They propose to combine multi-task learning problems with multi-lingual Transformers to model zero-shot transfer across languages. |
| Outcome: | The proposed model can predict zero-shot transfer across languages with a multi-task learning problem with pretraining data in very few languages. |
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models (2021.naacl-main)
Copied to clipboard
| Challenge: | a new study examines zero-shot cross-lingual transfer of vision-language models . we study multilingual text-to-video search in non-English languages without annotations . |
| Approach: | They propose a Transformer-based model that learns contextual multilingual multimodal embeddings . they propose 'zero-shot cross-lingual transfer' to improve multilingual search . |
| Outcome: | The proposed model outperforms baselines on multilingual text-to-video search and multilingual image search on VTT and VATEX. |
Multi-Task Transfer Matters During Instruction-Tuning (2024.findings-acl)
Copied to clipboard
| Challenge: | Instruction-tuning improves a model’s ability to learn in-context, but the mechanisms that drive in-constext learning are poorly understood. |
| Approach: | They propose to train a model on hundreds of tasks to improve its ability to learn in-context. |
| Outcome: | The proposed methods improve model transfer and in-context generalization, suggesting catastrophic forgetting may impact in-constext learning. |
Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuning (2023.acl-short)
Copied to clipboard
| Challenge: | Recent studies on instruction tuning (IT) have achieved great performance with zero-shot generalizability to unseen tasks. |
| Approach: | They analyze how models utilize instructions during IT by comparing model training with altered vs. original instructions. |
| Outcome: | The proposed model outperforms naive models in low resource setting. |
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
| Approach: | They propose to fine-tune pre-trained language models to optimize the zero-shot learning objective by aggregating 43 existing datasets and annotating 441 label descriptions in a question-answering format. |
| Outcome: | The proposed model outperforms a same-sized QA model and the previous SOTA zero-shot learning system on unseen tasks. |
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent work has shown that fine-tuning large language models on large instruction-following datasets improves their performance on a wide range of NLP tasks, but they fail to outperform small LMs on relation extraction (RE), a fundamental information extraction task. |
| Approach: | They propose a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. |
| Outcome: | The proposed framework outperforms small LLMs on relation extraction (RE), a fundamental information extraction task, by a large margin. |