Papers with context learning
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification (2025.acl-short)
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| Challenge: | Current intent classification systems face significant challenges due to the vast number of possible intents and significant semantic overlap among similar intent classes. |
| Approach: | They propose a dynamic label refinement method that retrieves relevant examples for a test input and leverages a large language model to dynamically refine intent labels based on semantic understanding. |
| Outcome: | The proposed method resolves confusion between semantically similar intents and generates more interpretable intent labels. |
On-the-Fly Fusion of Large Language Models and Machine Translation (2024.findings-naacl)
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| Challenge: | a weaker-at-translation LLM can improve translations of a NMT model, compared to a strong dedicated model. |
| Approach: | They propose to ensemble a neural machine translation model with a large language model, prompted on the same task and input. |
| Outcome: | The proposed method can be combined with various techniques from LLM prompting, such as in context learning and translation context. |
DLM: A Decoupled Learning Model for Long-tailed Polyphone Disambiguation in Mandarin (2024.naacl-long)
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| Challenge: | Grapheme-to-phoneme conversion datasets suffer from the long-tail problem . context learning for polyphonic characters often stems from a single dimension . |
| Approach: | They propose a model for long-tailed polyphone disambiguation in Mandarin that decouples representation and classification learnings. |
| Outcome: | The proposed model can decouple representation and classification learnings . it achieves transition learning of context from local to global . |
VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation Agents (2026.acl-long)
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| Challenge: | Multimodal Large Language Models have demonstrated remarkable capabilities across vision-language tasks, but their performance as embodied agents needs further exploration. |
| Approach: | They propose a framework to evaluate multimodal large language models as zero-shot agents . they find that enhancing prevalent agents with Chain-of-Thought reasoning and self-reflection leads to an unexpected performance decrease. |
| Outcome: | The proposed framework enables comparisons and component-level ablations across diverse MLLM architectures, agent designs, and navigation tasks. |