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Found 14 papers, 12 papers with code
LLatrieval: LLM-Verified Retrieval for Verifiable Generation
Thus, the LLM can iteratively provide feedback to retrieval and facilitate the retrieval result to fully support verifiable generation.
MoT: Memory-of-Thought Enables ChatGPT to Self-Improve
Specifically, MoT is divided into two stages: 1. before the test stage, the LLM pre-thinks on the unlabeled dataset and saves the high-confidence thoughts as external memory; 2.
Unified Demonstration Retriever for In-Context Learning
To train UDR, we cast various tasks' training signals into a unified list-wise ranking formulation by language model's feedback.
Finding Support Examples for In-Context Learning
Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible.
Soft-Labeled Contrastive Pre-training for Function-level Code Representation
In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level \textbf{Code} \textbf{R}epresentation.
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition
In this paper, we propose using Convolutional Neural Network (CNN) to model these spatial relations in the score matrix.
Ranked #3 on
Nested Named Entity Recognition
on ACE 2005
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search
For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build code-text pairs.
Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning
\textbf{P}re-\textbf{T}rained \textbf{M}odel\textbf{s} have been widely applied and recently proved vulnerable under backdoor attacks: the released pre-trained weights can be maliciously poisoned with certain triggers.
Accelerating BERT Inference for Sequence Labeling via Early-Exit
To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks.
BERT for Monolingual and Cross-Lingual Reverse Dictionary
Reverse dictionary is the task to find the proper target word given the word description.
FLAT: Chinese NER Using Flat-Lattice Transformer
Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information.
Ranked #5 on
Chinese Named Entity Recognition
on MSRA
Chinese Named Entity Recognition
named-entity-recognition
+3
Learning Sparse Sharing Architectures for Multiple Tasks
Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing.
TENER: Adapting Transformer Encoder for Named Entity Recognition
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
Ranked #11 on
Chinese Named Entity Recognition
on Resume NER
Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
Introducing common sense to natural language understanding systems has received increasing research attention.
