20 Q : Overlap-Free World Knowledge Benchmark for Language Models (2022)

Abstract: What do language models know about our world? This question is hard to answer but important to get right. To this end, we introduce 20Q, a novel benchmark using the Twenty Questions game to evaluate world knowledge and common sense of language models. Thanks to our overlap-free benchmark, language models learn the game of Twenty Questions without learning relevant knowledge for the test set. We uncover two intuitive factors influencing the world knowledge of language models: the size of the model and the topic frequency in the pre-training data. Moreover, we show that in-context learning is inefficient for evaluating language models’ world knowledge — fine-tuning is necessary to show their true capabilities. Lastly, our results show room for improvement to enhance the world knowledge and common sense of large language models. A potential solution would be to up-sample unfrequent topics in the pre-training of language models.

## introduction transformers are omnipresent in today's natural language processing. using a simple training and inference procedure, they reach human-level performance on numerous benchmarks. the scale of these models is hard to grasp. the most recent one, palm @xcite , has 540 billion parameters. it has sixteen times more parameters than all words on wikipedia, or sixty-eight times more parameters than the total population on earth @xcite . much previous work focused on what these models can do: question-answering, mathematics, translation, or code generation @xcite @xcite @xcite . another exciting area of research is to focus on what these models know: common sense, world knowledge, or biases topic question answer gorilla is it alive? yes ball can we eat it? no anchor is it heavy? yes pen can it fly? no car can you drive it? yes satellite it it furniture? no transformers @xcite models do not store knowledge symbolically -they distribute the knowledge within their weights. as a result, researchers have to use proxy tasks to study it. previous research used closed-book question-answering datasets to study how much knowledge language models can store @xcite . they concluded that language models perform similarly with or without external information, thanks to a broad embedded knowledge. unfortunately, @xcite later demonstrated that these datasets suffer from a significant overlap between the training and test set. for example, who has scored more goals in the premier league shares the same answer with most goals scored by a premier league player. training on the first and evaluating on the second does not make sense. as a result, t5's @xcite performance dramatically dropped when @xcite removed the overlap -invalidating the conclusion that these models performed equally with or without external knowledge. our analysis reveals commonsense reasoning benchmarks also display major overlap between the training and test sets. commonsense qa 2.0 @xcite and com2sense @xcite have exact or closeto-exact duplicates between the training and test set. in this work, we propose a new benchmark, free of any lexical and semantic overlap between the training and test set, to evaluate the world knowledge of large language models using the game of twenty questions -a popular yes/no guessing game. see we test two hypotheses using this benchmark. first, we test whether large models possess more world knowledge that smaller models. second, we test our intuition that world knowledge is correlated with the frequency of the topic in language models' pre-training data. despite the massive size of gpt-3, it only reaches an f1 score of 82% on our benchmark. it is however much better than its smaller variants, which validates our first hypothesis that larger models possess more world knowledge than smaller models. our dataset's unique feature -a generic question and a topic -is ideal for testing our second hypothesis: does world knowledge correlate with topic frequency. again, the results show our hypothesis is true as the bottom quartile of topics is associated with higher variability, whereas the other quartiles are not. we conclude this introduction by summarizing our main contributions: we release our benchmark on the huggingface dataset hub @xcite for anyone to use. 1 1 https://huggingface.co/datasets/clips/20q 2 related work before the rise of deep learning, nlp stored commonsense and world knowledge using semantic networks such as wordnet @xcite and later conceptnet @xcite . these graphs have the advantage of using symbolic representations, facilitating their analysis. contrary to transformersbased models, they perform equally well on lowerfrequency topics. commonsense and world knowledge of transformers' based models is harder to evaluate, researchers resort to using proxy tasks to evaluate it. several previous works studied the commonsense abilities of language models in multiple areas: pronoun resolution @xcite , natural language generation (lin et al., 2020), story understanding @xcite , reading comprehension @xcite @xcite , physical and social intelligence @xcite , temporal reasoning @xcite , numerical knowledge @xcite , and global commonsense reasoning @xcite @xcite . the remainder of this section focuses on two datasets evaluation the commonsense knowledge of language models using yes/no questions: commonsense qa 2.0 @xcite and com2sense @xcite . for both of these datasets, we review the overlap between the training and test set and find troubling examples. ## language model after reviewing that data, we review the language models. although previous work used text-to-text models such as t5 @xcite , t0 @xcite , and bart @xcite , in this work, we stick to @xcite , a general-purpose decoder-only transformers language model. by sticking to a single model, we can ensure that the only differentiating factor between the models is the network size, not the pre-training data or model architecture. ## experiments ## results we run all experiments and report binary-f1 and negative log-likelihood (nll) to the groundtruth answers in ## conclusion previous research @xcite showed that language models do not have enough world knowledge to rival open-domain question-answering systems. we update this claim using larger models and a novel benchmark, 20q. we find two factors influencing the world knowledge of language models: the model's size and the topic's frequency in the pre-training data. thanks to careful attention to the overlap between the training and validation set, we can safely conclude that fine-tuning provides a better picture of the world knowledge possessed by language models. our benchmark shows that even the largest language models (175 billion parameters) have room for improvement regarding world knowledge. we propose several areas of improvement for coping with a rapidly changing world as future work. step size (abs) size (%) initial dataset 78,890 100 low scores -12,396 -15.7 do not use "it" -9,665 -12.3 duplicates -2,708 -3.4 wordnet -2,312 -2.9 clean dataset 51,809 65.7