Papers with parameter learning

3 papers
RAV: Retrieval-Augmented Voting for Tactile Descriptions Without Training (2025.emnlp-main)

Copied to clipboard

Challenge: Conventional approaches relying on extensive parameter learning for multimodal perception are rigid and computationally inefficient.
Approach: They propose a parameter-free method that constructs visual-tactile cross-modal knowledge directly by retrieving similar visual-touch data for given visual and tactile inputs and generating tactile descriptions through a voting mechanism.
Outcome: The proposed method achieves comparable performance to large-scale cross-modal models without training across a wide range of datasets.
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars (2023.acl-long)

Copied to clipboard

Challenge: a recent study shows that context-free grammars are not natural for modeling discontinuous language phenomena such as extrapositions and cross-serial dependencies.
Approach: They propose a grammar induction approach with mildly context-sensitive grammars for unsupervised discontinuous parsing.
Outcome: Experiments on German and Dutch show that the proposed grammar induction method is beneficial for unsupervised parsing.
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text (2023.acl-long)

Copied to clipboard

Challenge: Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-difference (ID) examples.
Approach: They propose a method that integrates strengths and weaknesses of both methods . they use a fine-tuned model as the teacher to teach a randomly initialized student model .
Outcome: The proposed method outperforms human evaluators in the pair-expert task on the Human ChatGPT Comparison Corpus.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations