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Found 7 papers, 5 papers with code
AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
Evaluating large language models (LLMs) as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications.
PsyBench: a balanced and in-depth Psychological Chinese Evaluation Benchmark for Foundation Models
As Large Language Models (LLMs) are becoming prevalent in various fields, there is an urgent need for improved NLP benchmarks that encompass all the necessary knowledge of individual discipline.
Contrastive Learning of Sentence Embeddings from Scratch
Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings.
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context.
Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings.
S-SimCSE: Sampled Sub-networks for Contrastive Learning of Sentence Embedding
The corresponding outputs, two sentence embeddings derived from the same sentence with different dropout masks, can be used to build a positive pair.
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.
