General Knowledge
90 papers with code • 1 benchmarks • 2 datasets
This task aims to evaluate the ability of a model to answer general-knowledge questions.
Source: BIG-bench
Benchmarks
These leaderboards are used to track progress in General Knowledge
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Libraries
Use these libraries to find General Knowledge models and implementations
BIG-bench
BEAR-probe
Subtasks
Most implemented papers
Transformers as Soft Reasoners over Language
However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research.
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
jindongwang/transferlearning
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Based on our previous MetaAdapter that implicitly leverages adapters, we propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters.
BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation
There is currently no simple, unified way to compare, analyse or evaluate metrics across a representative set of tasks.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
CC-Riddle: A Question Answering Dataset of Chinese Character Riddles
Solving Chinese character riddles is a challenging task that demands understanding of character glyph, general knowledge, and a grasp of figurative language.
Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling
PaddlePaddle/PaddleDetection
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In this paper, we focus on the compression of DETR with knowledge distillation.
Adapting a Language Model While Preserving its General Knowledge
uic-liu-lab/dga
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This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge.
Continual Pre-training of Language Models
A novel proxy is also proposed to preserve the general knowledge in the original LM.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.
