Papers with random accuracy
Measuring Social Norms of Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing datasets that evaluate a general understanding of social science are inadequate to understand social norms. |
| Approach: | They propose a multi-agent framework to improve large language models’ ability to understand social norms by comparing them to elementary students. |
| Outcome: | The proposed framework improves large language models to be on par with humans. |
Are Prompt-based Models Clueless? (2022.acl-long)
Copied to clipboard
| Challenge: | Prompting has reduced the data requirement by reusing the language model head and formatting the task input to match the pre-training objective. |
| Approach: | They propose to examine whether few-shot prompt-based models exploit superficial cues by reusing the model head and formatting the input to match the pre-training objective. |
| Outcome: | The proposed models perform well on instances with superficial cues, but often outperform random accuracy on instances without superficial cuing. |
Probing Neural Network Comprehension of Natural Language Arguments (P19-1)
Copied to clipboard
| Challenge: | Argument Reasoning Comprehension Task (ARCT) focuses on inferences, not just discovering warrants. |
| Approach: | They propose to build an adversarial dataset on which all models achieve random accuracy. |
| Outcome: | The proposed dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work. |
Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)
Copied to clipboard
| Challenge: | a new adversarial method of paraphrase identification is being used to identify paraphrases based on word overlap and syntax . authors propose a dataset that generates semantically equivalent but lexically and syntactically disparate paraphrase pairs . |
| Approach: | They propose an adversarial method for paraphrase identification that uses word overlap and syntax to identify paraphrases. |
| Outcome: | The proposed method improves paraphrase detection accuracy and speed of generation of datasets. |