ymoslem/acl-abstracts huggingface dataset:
Algorithm for Automatic Legislative Text Consolidation (2024)

This study introduces a method for automating the consolidation process in a legal context, a time-consuming task traditionally performed by legal professionals. We present a generative approach that processes legislative texts to automatically apply amendments. Our method employs light quantized generative model, finetuned with LoRA, to generate accurate and reliable amended texts. To the authors knowledge, this is the first time generative models are used on legislative text consolidation. Our dataset is publicly available on HuggingFace. Experimental results demonstrate a significant improvement in efficiency, offering faster updates to legal documents. A full automated pipeline of legislative text consolidation can be done in a few hours, with a success rate of more than 63{\%} on a difficult bill.
Measuring the Groundedness of Legal Question-Answering Systems (2024)

In high-stakes domains like legal question-answering, the accuracy and trustworthiness of generative AI systems are of paramount importance. This work presents a comprehensive benchmark of various methods to assess the groundedness of AI-generated responses, aiming to significantly enhance their reliability. Our experiments include similarity-based metrics and natural language inference models to evaluate whether responses are well-founded in the given contexts. We also explore different prompting strategies for large language models to improve the detection of ungrounded responses. We validated the effectiveness of these methods using a newly created grounding classification corpus, designed specifically for legal queries and corresponding responses from retrieval-augmented prompting, focusing on their alignment with source material. Our results indicate potential in groundedness classification of generated responses, with the best method achieving a macro-F1 score of 0.8. Additionally, we evaluated the methods in terms of their latency to determine their suitability for real-world applications, as this step typically follows the generation process. This capability is essential for processes that may trigger additional manual verification or automated response regeneration. In summary, this study demonstrates the potential of various detection methods to improve the trustworthiness of generative AI in legal settings.
Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs (2024)

This paper presents a novel approach to legal judgment prediction by combining BERT embeddings with a Delaunay-based Graph Neural Network (GNN). Unlike inductive methods that classify legal documents independently, our transductive approach models the entire document set as a graph, capturing both contextual and relational information. This method significantly improves classification accuracy by enabling effective label propagation across connected documents. Evaluated on the Swiss-Judgment-Prediction (SJP) dataset, our model outperforms established baselines, including larger models with cross-lingual training and data augmentation techniques, while maintaining efficiency with minimal computational overhead.
Cross Examine: An Ensemble-based approach to leverage Large Language Models for Legal Text Analytics (2024)

Legal documents are complex in nature, describing a course of argumentative reasoning that is followed to settle a case. Churning through large volumes of legal documents is a daily requirement for a large number of professionals who need access to the information embedded in them. Natural language processing methods that help in document summarization with key information components, insight extraction and question answering play a crucial role in legal text processing. Most of the existing document analysis systems use supervised machine learning, which require large volumes of annotated training data for every different application and are expensive to build. In this paper we propose a legal text analytics pipeline using Large Language Models (LLM), which can work with little or no training data. For document summarization, we propose an iterative pipeline using retrieval augmented generation to ensure that the generated text remains contextually relevant. For question answering, we propose a novel ontology-driven ensemble approach similar to cross-examination that exploits questioning and verification principles. A knowledge graph, created with the extracted information, stores the key entities and relationships reflecting the repository content structure. A new dataset is created with Indian court documents related to bail applications for cases filed under Protection of Children from Sexual Offences (POCSO) Act, 2012 an Indian law to protect children from sexual abuse and offences. Analysis of insights extracted from the answers reveal patterns of crime and social conditions leading to those crimes, which are important inputs for social scientists as well as legal system.
LLMs to the Rescue: Explaining DSA Statements of Reason with Platform`s Terms of Services (2024)

The Digital Services Act (DSA) requires online platforms in the EU to provide {\textquotedblleft}statements of reason{\textquotedblright} (SoRs) when restricting user content, but their effectiveness in ensuring transparency is still debated due to vague and complex terms of service (ToS). This paper explores the use of NLP techniques, specifically multi-agent systems based on large language models (LLMs), to clarify SoRs by linking them to relevant ToS sections. Analysing SoRs from platforms like Booking.com, Reddit, and LinkedIn, our findings show that LLMs can enhance the interpretability of content moderation decisions, improving user understanding and engagement with DSA requirements.
BLT: Can Large Language Models Handle Basic Legal Text? (2024)

We find that the best publicly available LLMs like GPT-4 and Claude currently perform poorly on basic legal text handling. This motivates the creation of a benchmark consisting of examples that lawyers and paralegals would expect LLMs to handle zero-shot, such as looking up the text at a line of a witness deposition or at a subsection of a contract. LLMs' poor performance on this benchmark casts into doubt their reliability as-is for legal practice. However, fine-tuning on our training set brings even a small model to near-perfect performance. This benchmark will be useful for fine-tuning LLMs for downstream legal tasks, as well as for tracking LLMs' reliability as-is for basic legal tasks.
Multi-Property Multi-Label Documents Metadata Recommendation based on Encoder Embeddings (2024)

The task of document classification, particularly multi-label classification, presents a significant challenge due to the complexity of assigning multiple relevant labels to each document. This complexity is further amplified in multi-property multi-label classification tasks, where documents must be categorized across various sets of labels. In this research, we introduce an innovative encoder embedding-driven approach to multi-property multi-label document classification that leverages semantic-text similarity and the reuse of pre-existing annotated data to enhance the efficiency and accuracy of the document annotation process. Our method requires only a single model for text similarity, eliminating the need for multiple property-specific classifiers and thereby reducing computational demands and simplifying deployment. We evaluate our approach through a prototype deployed for daily operations, which demonstrates superior performance over existing classification systems. Our contributions include improved accuracy without additional training, increased efficiency, and demonstrated effectiveness in practical applications. The results of our study indicate the potential of our approach to be applied across various domains requiring multi-property multi-label document classification, offering a scalable and adaptable solution for metadata annotation tasks.
Comparative Study of Explainability Methods for Legal Outcome Prediction (2024)

This paper investigates explainability in Natural Legal Language Processing (NLLP). We study the task of legal outcome prediction of the European Court of Human Rights cases in a ternary classification setup, where a language model is fine-tuned to predict whether an article has been claimed and violated (positive outcome), claimed but not violated (negative outcome) or not claimed at all (null outcome). Specifically, we experiment with three popular NLP explainability methods. Correlating the attribution scores of input-level methods (Integrated Gradients and Contrastive Explanations) with rationales from court rulings, we show that the correlations are very weak, with absolute values of Spearman and Kendall correlation coefficients ranging between 0.003 and 0.094. Furthermore, we use a concept-level interpretability method (Concept Erasure) with human expert annotations of legal reasoning, to show that obscuring legal concepts from the model representation has an insignificant effect on model performance (at most a decline of 0.26 F1). Therefore, our results indicate that automated legal outcome prediction models are not reliably grounded in legal reasoning.
Bonafide at LegalLens 2024 Shared Task: Using Lightweight DeBERTa Based Encoder For Legal Violation Detection and Resolution (2024)

In this work, we present two systems{---}Named Entity Resolution (NER) and Natural Language Inference (NLI){---}for detecting legal violations within unstructured textual data and for associating these violations with potentially affected individuals, respectively. Both these systems are lightweight DeBERTa based encoders that outperform the LLM baselines. The proposed NER system achieved an F1 score of 60.01{\%} on Subtask A of the LegalLens challenge, which focuses on identifying violations. The proposed NLI system achieved an F1 score of 84.73{\%} on Subtask B of the LegalLens challenge, which focuses on resolving these violations by matching them with pre-existing legal complaints of class action cases. Our NER system ranked sixth and NLI system ranked fifth on the LegalLens leaderboard. We release the trained models and inference scripts.
LAR-ECHR: A New Legal Argument Reasoning Task and Dataset for Cases of the European Court of Human Rights (2024)

We present Legal Argument Reasoning (LAR), a novel task designed to evaluate the legal reasoning capabilities of Large Language Models (LLMs). The task requires selecting the correct next statement (from multiple choice options) in a chain of legal arguments from court proceedings, given the facts of the case. We constructed a dataset (LAR-ECHR) for this task using cases from the European Court of Human Rights (ECHR). We evaluated seven general-purpose LLMs on LAR-ECHR and found that (a) the ranking of the models is aligned with that of LegalBench, an established US-based legal reasoning benchmark, even though LAR-ECHR is based on EU law, (b) LAR-ECHR distinguishes top models more clearly, compared to LegalBench, (c) even the best model (GPT-4o) obtains 75.8{\%} accuracy on LAR-ECHR, indicating significant potential for further model improvement. The process followed to construct LAR-ECHR can be replicated with cases from other legal systems.
Gaps or Hallucinations? Scrutinizing Machine-Generated Legal Analysis for Fine-grained Text Evaluations (2024)

Large Language Models (LLMs) show promise as a writing aid for professionals performing legal analyses. However, LLMs can often hallucinate in this setting, in ways difficult to recognize by non-professionals and existing text evaluation metrics. In this work, we pose the question: when can machine-generated legal analysis be evaluated as acceptable? We introduce the neutral notion of gaps {--} as opposed to hallucinations in a strict erroneous sense {--} to refer to the difference between human-written and machine-generated legal analysis. Gaps do not always equate to invalid generation. Working with legal experts, we consider the CLERC generation task proposed in Hou et al. (2024b), leading to a taxonomy, a fine-grained detector for predicting gap categories, and an annotated dataset for automatic evaluation. Our best detector achieves 67{\%} F1 score and 80{\%} precision on the test set. Employing this detector as an automated metric on legal analysis generated by SOTA LLMs, we find around 80{\%} contain hallucinations of different kinds.
Classify First, and Then Extract: Prompt Chaining Technique for Information Extraction (2024)

This work presents a new task-aware prompt design and example retrieval approach for information extraction (IE) using a prompt chaining technique. Our approach divides IE tasks into two steps: (1) text classification to understand what information (e.g., entity or event types) is contained in the underlying text and (2) information extraction for the identified types. Initially, we use a large language model (LLM) in a few-shot setting to classify the contained information. The classification output is used to select the relevant prompt and retrieve the examples relevant to the input text. Finally, we ask a LLM to do the information extraction with the generated prompt. By evaluating our approach on legal IE tasks with two different LLMs, we demonstrate that the prompt chaining technique improves the LLM`s overall performance in a few-shot setting when compared to the baseline in which examples from all possible classes are included in the prompt. Our approach can be used in a low-resource setting as it does not require a large amount of training data. Also, it can be easily adapted to many different IE tasks by simply adjusting the prompts. Lastly, it provides a cost benefit by reducing the number of tokens in the prompt.
Augmenting Legal Decision Support Systems with LLM-based NLI for Analyzing Social Media Evidence (2024)

This paper presents our system description and error analysis of our entry for NLLP 2024 shared task on Legal Natural Language Inference (L-NLI). The task required classifying these relationships as entailed, contradicted, or neutral, indicating any association between the review and the complaint. Our system emerged as the winning submission, significantly outperforming other entries with a substantial margin and demonstrating the effectiveness of our approach in legal text analysis. We provide a detailed analysis of the strengths and limitations of each model and approach tested, along with a thorough error analysis and suggestions for future improvements. This paper aims to contribute to the growing field of legal NLP by offering insights into advanced techniques for natural language inference in legal contexts, making it accessible to both experts and newcomers in the field.
Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights (2024)

The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collection of documents detailing air travel regulations. The most relevant passages from these documents are presented along with links to the original documents and the generated queries, enabling users to dissect and leverage the information for their unique circumstances. The system successfully overcomes two predominant challenges: understanding complex user inputs, and delivering accurate answers, free of hallucinations, that passengers can rely on for making informed decisions. A user study comparing the chatbot to a Google search demonstrated the chatbot`s usefulness and ease of use. Beyond the primary goal of providing accurate and timely information to air passengers regarding their rights, we hope that this system will also enable further research exploring the tradeoff between the user-friendly conversational interface of chatbots and the accuracy of retrieval systems.
Enhancing Legal Violation Identification with LLMs and Deep Learning Techniques: Achievements in the LegalLens 2024 Competition (2024)

LegalLens is a competition organized to encourage advancements in automatically detecting legal violations. This paper presents our solutions for two tasks Legal Named Entity Recognition (L-NER) and Legal Natural Language Inference (L-NLI). Our approach involves fine-tuning BERT-based models, designing methods based on data characteristics, and a novel prompting template for data augmentation using LLMs. As a result, we secured first place in L-NER and third place in L-NLI among thirty-six participants. We also perform error analysis to provide valuable insights and pave the way for future enhancements in legal NLP. Our implementation is available at https://github.com/lxbach10012004/legal-lens/tree/main
LegalLens 2024 Shared Task: Masala-chai Submission (2024)

In this paper, we present the masala-chai team`s participation in the LegalLens 2024 shared task and detail our approach to predicting legal entities and performing natural language inference (NLI) in the legal domain. We experimented with various transformer-based models, including BERT, RoBERTa, Llama 3.1, and GPT-4o. Our results show that state-of-the-art models like GPT-4o underperformed in NER and NLI tasks, even when using advanced techniques such as bootstrapping and prompt optimization. The best performance in NER (accuracy: 0.806, F1 macro: 0.701) was achieved with a fine-tuned RoBERTa model, while the highest NLI results (accuracy: 0.825, F1 macro: 0.833) came from a fine-tuned Llama 3.1 8B model. Notably, RoBERTa, despite having significantly fewer parameters than Llama 3.1 8B, delivered comparable results. We discuss key findings and insights from our experiments and provide our results and code for reproducibility and further analysis at https://github.com/rosequ/masala-chai
Semantists at LegalLens-2024: Data-efficient Training of LLM`s for Legal Violation Identification (2024)

In this paper, we describe our system for LegalLens-2024 Shared Task on automatically identifying legal violations from unstructured text sources. We participate in Subtask B, called Legal Natural Language Inference (L-NLI), that aims to predict the relationship between a given premise summarizing a class action complaint and a hypothesis from an online media text, indicating any association between the review and the complaint. This task is challenging as it provides only limited labelled data. In our work, we adopt LLM based methods and explore various data-efficient learning approaches for maximizing performance. In the end, our best model employed an ensemble of LLM`s fine-tuned on the task-specific data, and achieved a Macro F1 score of 78.5{\%} on test data, and ranked 2nd among all teams submissions.
LegalLens Shared Task 2024: Legal Violation Identification in Unstructured Text (2024)

This paper presents the results of the LegalLens Shared Task, focusing on detecting legal violations within text in the wild across two sub-tasks: LegalLens-NER for identifying legal violation entities and LegalLens-NLI for associating these violations with relevant legal contexts and affected individuals. Using an enhanced LegalLens dataset covering labor, privacy, and consumer protection domains, 38 teams participated in the task. Our analysis reveals that while a mix of approaches was used, the top-performing teams in both tasks consistently relied on fine-tuning pre-trained language models, outperforming legal-specific models and few-shot methods. The top-performing team achieved a 7.11{\%} improvement in NER over the baseline, while NLI saw a more marginal improvement of 5.7{\%}. Despite these gains, the complexity of legal texts leaves room for further advancements.
DeBERTa Beats Behemoths: A Comparative Analysis of Fine-Tuning, Prompting, and PEFT Approaches on LegalLensNER (2024)

This paper summarizes the participation of our team (Flawless Lawgic) in the legal named entity recognition (L-NER) task at LegalLens 2024: Detecting Legal Violations. Given possible unstructured texts (e.g., online media texts), we aim to identify legal violations by extracting legal entities such as {\textquotedblleft}violation{\textquotedblright}, {\textquotedblleft}violation by{\textquotedblright}, {\textquotedblleft}violation on{\textquotedblright}, and {\textquotedblleft}law{\textquotedblright}. This system-description paper discusses our approaches to address the task, empirically highlighting the performances of fine-tuning models from the Transformers family (e.g., RoBERTa and DeBERTa) against open-sourced LLMs (e.g., Llama, Mistral) with different tuning settings (e.g., LoRA, Supervised Fine-Tuning (SFT) and prompting strategies). Our best results, with a weighted F1 of 0.705 on the test set, show a 30 percentage points increase in F1 compared to the baseline and rank 2 on the leaderboard, leaving a marginal gap of only 0.4 percentage points lower than the top solution. Our solutions are available at github.com/honghanhh/lner.
LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English (2024)

In the evolving NLP landscape, benchmarks serve as yardsticks for gauging progress. However, existing Legal NLP benchmarks only focus on predictive tasks, overlooking generative tasks. This work curates LexSumm, a benchmark designed for evaluating legal summarization tasks in English. It comprises eight English legal summarization datasets, from diverse jurisdictions, such as the US, UK, EU and India. Additionally, we release LexT5, legal oriented sequence-to-sequence model, addressing the limitation of the existing BERT-style encoder-only models in the legal domain. We assess its capabilities through zero-shot probing on LegalLAMA and fine-tuning on LexSumm. Our analysis reveals abstraction and faithfulness errors even in summaries generated by zero-shot LLMs, indicating opportunities for further improvements. LexSumm benchmark and LexT5 model are available at https://github.com/TUMLegalTech/LexSumm-LexT5.
Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need? (2024)

Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI {\&} Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually {\textquotedblleft}simpler', these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI {\&} Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate conceptual legal knowledge.
Probing Large Language Models from a Human Behavioral Perspective (2024)

Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. However, the understanding of their prediction processes and internal mechanisms, such as feed-forward networks (FFN) and multi-head self-attention (MHSA), remains largely unexplored. In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely recognized as meaningful indicators of human reading patterns. Our findings reveal that LLMs exhibit a similar prediction pattern with humans but distinct from that of Shallow Language Models (SLMs). Moreover, with the escalation of LLM layers from the middle layers, the correlation coefficients also increase in FFN and MHSA, indicating that the logits within FFN increasingly encapsulate word semantics suitable for predicting tokens from the vocabulary.