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Found 5 papers, 2 papers with code
A Training Data Recipe to Accelerate A* Search with Language Models
Recent works in AI planning have proposed to combine LLMs with iterative tree-search algorithms like A* and MCTS, where LLMs are typically used to calculate the heuristic, guiding the planner towards the goal.
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification
While such methods have shown empirical success, we observe two key uncharted aspects, (i) DE training typically uses only a single positive relation even for datasets which offer more, (ii) existing approaches fixate on using only OvA reduction of the multi-label problem.
Learning label-label correlations in Extreme Multi-label Classification via Label Features
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices.
Extreme Multi-Label Classification
Multi Label Text Classification
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CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation
Simultaneously, there has been an influx of multilingual pre-trained models for NMT and multimodal pre-trained models for vision-language tasks, primarily in English, which have shown exceptional generalisation ability.
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification
Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation.
