Validity Assessment of Legal Will Statements as Natural Language Inference (2022.findings-emnlp)
Copied to clipboard
| Challenge: | This study introduces a dataset that focuses on the validity of statements in legal wills. |
| Approach: | They propose a dataset that focuses on the validity of statements in legal wills. |
| Outcome: | The proposed model achieves 80% macro F1 and accuracy, but group accuracy is in mid 80s at best, suggesting that the models’ understanding of the task remains superficial. |
Similar Papers
ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Contract review is a time-consuming procedure that costs companies millions of dollars each year . linguistic characteristics of contracts, such as negations by exceptions, contribute to the difficulty of this task . |
| Approach: | They propose a document-level natural language inference (NLI) task for contracts . they annotate and release the largest corpus to date consisting of 607 annotated contracts a linguistically rich system is proposed . |
| Outcome: | The proposed system is based on a contract review task that includes 607 annotated contracts. |
NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports (2023.emnlp-main)
Copied to clipboard
| Challenge: | Clinical trial reports (CTRs) are indispensable for the development of personalized medicine. |
| Approach: | They propose a resource to help researchers interpret clinical trial reports . they use natural language inference to compute textual entailment . |
| Outcome: | The proposed resource is the first to cover interpretation of full clinical trial reports . it includes tasks to determine inference relation between natural language statements and CTRs . |
Information Extraction from Legal Wills: How Well Does GPT-4 Do? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Using information extraction from legal wills is an important application of artificial intelligence (AI) |
| Approach: | They propose a manually annotated dataset for Information Extraction (IE) from legal wills . they also use it to evaluate the performance of large language models (LLMs) |
| Outcome: | The proposed dataset can be used to evaluate large language models on IE from legal wills . it shows that the model performs reasonably well, but inconsistent outputs and overgeneralization are observed . |
Stress Test Evaluation for Natural Language Inference (C18-1)
Copied to clipboard
| Challenge: | Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. |
| Approach: | They propose to use automatic stress tests to evaluate models' ability to make inferential decisions. |
| Outcome: | The proposed model performs well across genres of text, but lacks the ability to make inferential decisions. |
A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. |
| Approach: | They propose a framework to investigate LLMs' competence in the law domain by using similar cases and multi-choice options. |
| Outcome: | The proposed solutions can be extended to other domains to facilitate evaluations in other domain. |
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2022.acl-long)
Copied to clipboard
Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, Nikolaos Aletras
| Challenge: | Laws and their interpretations, legal arguments and agreements are typically expressed in writing. |
| Approach: | They propose a benchmark to evaluate model performance across legal NLU tasks . they also evaluate several generic and legal-oriented models . |
| Outcome: | The proposed model performs better across multiple tasks than previous models. |
Deep Learning for Natural Language Inference (N19-5)
Copied to clipboard
| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
Constructing a Japanese Verdict Prediction Dataset for Fact-Checking of LLM-Generated Texts (2026.acl-srw)
Copied to clipboard
Miwa Masano, Hirokazu Kiyomaru, Atsushi Keyaki, Kaito Horio, Rei Minamoto, Ribeka Keyaki, Kouta Nakayama, Hideyuki Tachibana, Daisuke Kawahara
| Challenge: | Text generated by Large Language Models (LLMs) may contain plausible but incorrect information known as hallucinations. |
| Approach: | They extend the label set for verdict prediction to capture claim-evidence relationships humans would commonly interpret as supported or refuted. |
| Outcome: | The proposed system improves F1 by 4 percentage points compared to baseline. |
A Legal Perspective on Training Models for Natural Language Processing (L18-1)
Copied to clipboard
| Challenge: | a significant concern in processing natural language data is the unclear legal status of the input and output data/resources. |
| Approach: | They examine which legal rules apply at relevant steps and how they affect the legal status of the results. |
| Outcome: | The proposed model training process is based on three scenarios . the analysis focuses on which legal rules apply and how they affect the legal status of the results . |
Legal Judgment Prediction: A Reflection on the State of the Art (2026.acl-long)
Copied to clipboard
| Challenge: | Legal Judgment Prediction (LJP) involves predicting judgment outcomes based on fact descriptions of cases. |
| Approach: | They propose to use argument trees to build automated legal judgment prediction systems that are trustworthy and can be used to predict cases. |
| Outcome: | The proposed model outperforms competitors on standard evaluation datasets and enables pluralistic values to be naturally expressed. |