Papers with ICL tasks
TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration (2025.emnlp-main)
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| Challenge: | Multimodal in-context learning (ICL) is a key mechanism for harnessing the capabilities of large vision–language models. |
| Approach: | They propose a transformer-based model with task-aware attention that dynamically configures ICL sequences. |
| Outcome: | Experiments on five LVLMs and nine datasets show that TACO surpasses baselines across diverse ICL tasks. |
IAD: In-Context Learning Ability Decoupler of Large Language Models in Meta-Training (2024.lrec-main)
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| Challenge: | In-context Learning (ICL) is a paradigm in which LLMs acquire task-specific knowledge by processing input-output pairs provided as prompts. |
| Approach: | They propose an In-context learning Ability Decoupler to separate ICL ability from general ability of LLMs in meta-training phase . they first identify parameters suitable for ICL by transference-driven gradient importance and propose a new max-margin loss to emphasize the separation of the two abilities. |
| Outcome: | The proposed model separates the ICL ability from the general ability of LLMs in the meta-training phase, where the I-related parameters are tuned to adapt for ICL tasks. |
On Many-Shot In-Context Learning for Long-Context Evaluation (2025.acl-long)
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| Challenge: | Existing benchmarks primarily evaluate long-context language models' retrieval capabilities. |
| Approach: | They propose a benchmark to evaluate long-context language models' retrieval capabilities by using MANYICLBENCH. |
| Outcome: | The proposed model performs better with additional demonstrations than translation and reasoning tasks. |