Papers with comparative learning

3 papers
Event-enhanced Retrieval in Real-time Search (2024.lrec-main)

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Challenge: Existing embedding-based retrieval models face the "semantic drift" problem . a low adoption rate of retrieval results is evident in real-time search scenarios .
Approach: They propose an embedding-based retrieval approach that enhances real-time retrieval performance by adding contrastive learning to the dual-encoder model.
Outcome: The proposed approach improves the dual-encoder model of traditional EBR.
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models (2023.findings-emnlp)

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Challenge: Existing methods to solve complex logical reasoning problems are cumbersome for language models.
Approach: They propose to use iterative methodology to construct a cognitive tree using language models . they propose to generate multiple responses by utilizing in-context examples .
Outcome: The proposed model achieves a performance level comparable to that of GPT-3.5 . the proposed model contains fewer parameters than 5% of the model with 175B parameters .
Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition (2023.acl-long)

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Challenge: a recent study shows that word acquisition is an efficient, supervised, and continual process.
Approach: They develop a computational process for word acquisition through comparative learning . they frame the acquisition of words as representation-symbol mapping .
Outcome: The proposed method can be used to learn the meaning of a word efficiently and efficiently.

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