World Knowledge
315 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
Use these libraries to find World Knowledge models and implementations
BEAR-probe
MM-Eval
Most implemented papers
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
huggingface/trl
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NeurIPS 2023
Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).
Measuring Massive Multitask Language Understanding
By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
huggingface/transformers
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NeurIPS 2020
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
Mistral 7B
We introduce Mistral 7B v0. 1, a 7-billion-parameter language model engineered for superior performance and efficiency.
REALM: Retrieval-Augmented Language Model Pre-Training
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.
Imagine This! Scripts to Compositions to Videos
ubc-vision/make-a-story
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ECCV 2018
Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
jonathanherzig/commonsenseqa
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NAACL 2019
To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering.
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs.
ASER: A Large-scale Eventuality Knowledge Graph
Understanding human's language requires complex world knowledge.
Dense X Retrieval: What Retrieval Granularity Should We Use?
We discover that the retrieval unit choice significantly impacts the performance of both retrieval and downstream tasks.
