Papers with representation selection

3 papers
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings (2025.findings-emnlp)

Copied to clipboard

Challenge: NER Retriever uses a user-defined type description to retrieve documents mentioning entities of that type.
Approach: They propose a zero-shot retrieval framework for ad-hoc Named Entity Recognition . a user-defined type description is used to retrieve documents mentioning entities of that type .
Outcome: The proposed framework outperforms lexical and dense retrieval baselines on three benchmarks.
DRES: Fake news detection by dynamic representation and ensemble selection (2025.emnlp-main)

Copied to clipboard

Challenge: Existing methods for text-based fake news detection are limited due to context sensitivity and generalization issues.
Approach: They propose a method that leverages instance hardness measures to estimate the classification difficulty for each news article across multiple textual feature representations.
Outcome: The proposed method significantly improves over state-of-the-art methods, confirming the effectiveness of representation selection based on instance hardness and dynamic ensemble selection.
A BERTology View of LLM Orchestrations: Token- and Layer-Selective Probes for Efficient Single-Pass Classification (2026.acl-long)

Copied to clipboard

Challenge: Modern LLM deployments are rarely a single model in isolation.
Approach: They propose a model that reuses computation already paid for by the serving LLM . they instantiate a template with pooling, a scoring-attention gate, and a downcast multi-head self-attention probe .
Outcome: The proposed model improves safety and sentiment benchmarks on dense and mixture-of-experts architectures while preserving near-serving latency.

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