Papers with classification layer
Text2Model: Text-based Model Induction for Zero-shot Image Classification (2024.findings-emnlp)
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| Challenge: | Existing approaches to zero-shot learning are limited in two ways: Query-dependence and richness of language description. |
| Approach: | They propose a task-agnostic approach to image classification using only text descriptions . they train a hypernetwork that receives class descriptions and outputs a multi-class model . |
| Outcome: | The proposed approach generates non-linear classifiers, handles rich textual descriptions, and may be adapted to produce lightweight models efficient enough for on-device applications. |
Fine-Grained Features-based Code Search for Precise Query-Code Matching (2025.coling-main)
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| Challenge: | Existing methods to locate code snippets from databases represent the semantics of code and query by averaging the features of each token and word. |
| Approach: | They propose a fine-grained code search model that consists of a cross-modal encoder, mapping layer and classification layer to capture fine-granular interactions between code and query. |
| Outcome: | The proposed model significantly outperforms existing methods across multiple programming language datasets. |
Token Prediction as Implicit Classification to Identify LLM-Generated Text (2023.emnlp-main)
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| Challenge: | a novel approach for identifying large language models (LLMs) involved in text generation is proposed . instead of adding an additional classification layer, we reframe the classification task as a next-token prediction task . |
| Approach: | They propose a novel approach for identifying large language models involved in text generation . instead of adding an additional classification layer, they reframe the task as a next-token prediction task . |
| Outcome: | The proposed method performs exceptionally well in the text classification task . it can distinguish distinctive writing styles among various LLMs even without an explicit classifier. |
Adapting Large Language Models for Character-based Augmentative and Alternative Communication (2025.findings-emnlp)
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| Challenge: | Most character language models predict subword tokens of variable length . |
| Approach: | They propose to use large pretrained character language models to make accurate character predictions. |
| Outcome: | The proposed method produces more accurate character predictions than classification models and n-gram models. |