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Found 5 papers, 4 papers with code
MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling
Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in Natural Language Processing (NLP) tasks.
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
Guided by human evaluation and analysis, we highlight the shortcomings of traditional metrics for both correctness and faithfulness.
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Our findings indicate that the simple similarity metric employed by retrievers is insufficient for retrieving all the necessary statements for reasoning.
An Analysis of Social Biases Present in BERT Variants Across Multiple Languages
Although large pre-trained language models have achieved great success in many NLP tasks, it has been shown that they reflect human biases from their pre-training corpora.
