Summary of Nlp at Uc Santa Cruz at Semeval-2024 Task 5: Legal Answer Validation Using Few-shot Multi-choice Qa, by Anish Pahilajani et al.
NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation using Few-Shot Multi-Choice QA
by Anish Pahilajani, Samyak Rajesh Jain, Devasha Trivedi
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents two approaches to solving the SemEval 2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure, which involves validating legal answers given an introduction to the case, a question, and an answer candidate. First, pre-trained BERT-based models were fine-tuned and found that domain knowledge training led to better performance. Second, few-shot prompting was performed on GPT models and reformulating the task as a multiple-choice QA task significantly improved model performance. The best submission achieved 7th place out of 20 using a BERT-based model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a special kind of computer problem called legal answer validation. It’s like trying to figure out if an answer is correct or not, given some information about a court case and a question. Two different methods were tested: one that used pre-trained models trained on specific knowledge related to the task, and another that rephrased the problem as a multiple-choice quiz. The results showed that using domain-specific knowledge and reformulating the task led to better performance. This could be useful for developing computers that can help with legal research or court decisions. |
Keywords
» Artificial intelligence » Bert » Few shot » Gpt » Prompting
