Papers by Santosh T.y.s.s
LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases (2025.findings-naacl)
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| Challenge: | Existing approaches overlook the underlying semantic intent in determining relevance with respect to a query case. |
| Approach: | They propose a method that generates intents in the form of legal concepts from a query case facts and then augments the query with these concepts to enhance models understanding of semantic intent. |
| Outcome: | The proposed approach generates intents in the form of legal concepts and augments the query with these concepts to enhance models understanding of semantic intent that dictates relavance. |
The Craft of Selective Prediction: Towards Reliable Case Outcome Classification - An Empirical Study on European Court of Human Rights Cases (2024.findings-emnlp)
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| Challenge: | Existing COC tasks prioritize high task performance over model reliability . however, large models exhibit overconfidence and Monte Carlo dropout methods produce reliable confidence estimates . |
| Approach: | They conduct an empirical investigation into how various design choices affect the reliability of COC models within the framework of selective prediction. |
| Outcome: | The proposed model is able to predict the outcome of a legal case based on the text of the case facts and is compared with other models using a pre-training corpus. |
Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification (2024.acl-long)
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| Challenge: | Existing methods for NLP calibration ignore inherent human label variation (HLV) split votes are a problem in high-stakes domains such as legal and medical decisions . |
| Approach: | They present a case outcome classification dataset with judges' vote distributions and build a taxonomy of disagreement with SV-specific subcategories. |
| Outcome: | The proposed model is compared against a judge vote distribution and assesses the alignment of perceived difficulty between models and humans. |
AQuAECHR: Attributed Question Answering for European Court of Human Rights (2025.findings-acl)
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| Challenge: | LLMs are widely used for information seeking, but their generated responses often suffer from hallucinations, hindering their widespread adoption in high stakes domains such as law. |
| Approach: | They propose to attribute legal question answering to an actual source to improve factuality and verifiability of the answer. |
| Outcome: | The proposed framework improves the factuality and verifiability of legal question answering by combining a dataset from ECHR case law guides with an LLM-based filtering pipeline. |
ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks (2024.acl-long)
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| Challenge: | Existing models overlook the temporal dimension in their training process, leading to suboptimal performance over time. |
| Approach: | They propose a training paradigm that trains models on chronological splits, preserving the temporal order of the data. |
| Outcome: | The proposed model fails to fit to recent data, despite continual learning and temporal invariant methods. |
LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law (2025.acl-industry)
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| Challenge: | Recent efforts focus on automatic summarization of individual cases, which condense the content of a single case, making it easier for legal professionals to grasp key points. |
| Approach: | They propose a pipeline to generate multi-case structured reports using entire body of case law on user-specified topics within the European Court of Human Rights. |
| Outcome: | The proposed pipeline generates structured reports that enhance efficient, scalable legal analysis. |
From Naturalness to Norms: Interactional Cultural Competence for SpeechLMs (2026.acl-long)
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| Challenge: | Spoken language models are increasingly real-time conversational actors. |
| Approach: | They propose a speech-first view of cultural competence as interactional competence . they synthesize social-science foundations into a taxonomy of culture-bearing signals in speech . |
| Outcome: | The proposed model is based on a theory-derived taxonomy of culture-bearing signals in speech . it shows that cultural appropriateness is not a generic human-likeness . |
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (2025.findings-naacl)
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| Challenge: | Current approaches to legal summarization struggle with content theme deviation and inconsistent writing styles due to the content of the source document. |
| Approach: | They propose a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. |
| Outcome: | The proposed model outperforms models that do not utilize exemplars and those that rely on similarity-based exemplar selection. |
Position: From Noise to Signal to Selbstzweck - Reframing Human Label Variation in the Era of Post-training in NLP (2026.findings-acl)
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| Challenge: | Human Label Variation (HLV) refers to legitimate disagreement in annotation . current preference-learning datasets routinely collapse multiple annotations into a single label . |
| Approach: | They propose to preserve human label variation as an embodiment of pluralism . they argue that disagreement in annotations should be treated as a selfzweck . |
| Outcome: | The proposed approach preserves pluralism and human pluralismos, the authors argue . they argue that disagreements in annotations should be treated as a selfzweck . |
Fairness Beyond Performance: Revealing Reliability Disparities Across Groups in Legal NLP (2025.acl-long)
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| Challenge: | a recent study shows that models often make less reliable or overconfident predictions for marginalized groups. |
| Approach: | They evaluate performance and reliability disparities across demographic, regional, and legal attributes across four jurisdictions using the FairLex benchmark. |
| Outcome: | The FairLex benchmark shows that pre-training improves performance and reliability for underrepresented groups. |
CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation (2025.acl-long)
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Santosh T.y.s.s, Youssef Tarek Elkhayat, Oana Ichim, Pranav Shetty, Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh, Xiaomo Liu
| Challenge: | LLMs can provide key benefits to the Legal domain, but their adoption has been hindered by their tendency to generate unfaithful, ungrounded, or hallucinatory outputs. |
| Approach: | They propose a Confidence-guided copy-based decoding strategy that dynamically interpolates the model produced vocabulary distribution with a distribution derived based on copying from the context. |
| Outcome: | The proposed method outperforms existing context-aware decoding methods on five legal benchmarks. |
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases (2023.eacl-main)
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| Challenge: | a new method for case outcome classification is being developed for the European Court of Human Rights. |
| Approach: | They propose to use case facts descriptions to classify whether a court finds a violation of conventions. |
| Outcome: | The proposed model improves on single-task and joint models without contrastive loss. |
A Tale of Two Revisions: Summarizing Changes Across Document Versions (2024.findings-acl)
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| Challenge: | Document revision is a crucial aspect of the writing process, especially in collaborative environments where multiple authors contribute simultaneously. |
| Approach: | They propose a task of providing thematic summary of changes between document versions, organizing individual edits based on shared themes, and propose three strategies to tackle this task. |
| Outcome: | The proposed model improves its capacity to handle the task and also enables it to be used in a curated dataset. |
From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification (2023.emnlp-main)
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| Challenge: | Existing work in explainable COC has been limited to annotations by a single expert. |
| Approach: | They construct a two-level task-independent taxonomy from a dataset obtained from two experts in the domain of international human rights law . they find disagreements stem from underspecification of the legal context . |
| Outcome: | The proposed dataset is the first in legal NLP that focuses on human label variation. |
HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents (2024.findings-emnlp)
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| Challenge: | Existing approaches overlook the varying difficulty levels inherent in legal document discourse styles and rhetorical roles. |
| Approach: | They propose a hierarchical curriculum learning framework for RRL that nests two curricula: Rhetorical Role-level Curriculum (RC) on the outer layer and Document-level curriculum (DC) on inner layer. |
| Outcome: | The proposed framework is based on four legal document datasets and shows that it is complementary to existing models. |
Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases (2024.findings-emnlp)
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| Challenge: | Inspired by the legal doctrine of stare decisis, we explore methods to integrate precedents into LJP models. |
| Approach: | They propose to integrate precedents into legal judgment prediction models by integrating them at inference and during training via a precedent fusion module. |
| Outcome: | The proposed model outperforms models without precedents or with precedents incorporated only at inference on LJP tasks. |
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases (2023.findings-eacl)
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| Challenge: | Legal Judgment Prediction (LJP) is a classification task that uses textual descriptions of case facts as the input. |
| Approach: | They propose to use legal reasoning to map article text to specific case fact text to improve the model's generalization to zero-shot settings. |
| Outcome: | The proposed model outperforms straightforward fact classification and improves zero-shot transfer performance. |
QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval (2025.coling-main)
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| Challenge: | Existing methods for Statutory Article Retrieval (SAR) are vague and underspecified . however, a new approach is needed to bridge the gap between legal expertise and public understanding . |
| Approach: | They propose a framework for statutory article retrieval that leverages bipartite interactions between queries and articles to capture diverse aspects inherent in them. |
| Outcome: | The proposed framework overcomes the semantic mismatch problem when modeling each query-article pair in isolation. |
CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization (2025.findings-naacl)
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| Challenge: | Recent efforts to produce concise legal summarization have shifted towards abstractive approaches . |
| Approach: | They propose a framework that integrates content selection and planning components to generate coherent summaries based on both the content and the structured plan. |
| Outcome: | The proposed framework shows that it integrates content selection and planning components over entity-centric approaches in the context of legal judgements. |
ProMALex: Progressive Modular Adapters for Multi-Jurisdictional Legal Language Modeling (2025.acl-long)
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| Challenge: | Existing approaches to training language models for each jurisdiction fail to leverage common legal principles beneficial for low-resource settings or risk negative interference from conflicting jurisdictional interpretations. |
| Approach: | They propose a parameter-efficient framework that derives hierarchical relationships across jurisdictions and progressively inserts adapter modules across model layers based on jurisdictional similarity. |
| Outcome: | The proposed framework outperforms fully shared and jurisdiction-specific models on two legal language modeling benchmarks. |
Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts (2022.emnlp-main)
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| Challenge: | Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals. |
| Approach: | They propose to use domain expertise to identify statistically predictive but legally irrelevant information and adopt adversarial training to prevent it from relying on it. |
| Outcome: | The proposed model aligns better with expert rationales than baseline models . the results are compared with an existing benchmark dataset of human rights cases . |
Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization (2024.naacl-long)
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| Challenge: | a study explores the cross-jurisdictional generalizability of legal case summarization models . fine-tuning on non-target datasets outperforms unsupervised methods, but success depends on similarity between source and target jurisdictions. |
| Approach: | They explore how to effectively summarize legal cases of a target jurisdiction where reference summaries are not available. |
| Outcome: | The proposed model can be generalized across jurisdictions and improve transfer performance. |
VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights (2023.emnlp-main)
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| Challenge: | Existing work on the concept of vulnerability at the European Court of Human Rights (ECtHR) has focused on classification and analysis of textual data. |
| Approach: | They propose to use an expert-annotated multi-label dataset to assess vulnerability in court cases. |
| Outcome: | The proposed model performs poorly on out-of-domain data and shows that it is robust. |
LexKeyPlan: Planning with Keyphrases and Retrieval Augmentation for Legal Text Generation: A Case Study on European Court of Human Rights Cases (2025.acl-short)
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| Challenge: | Large language models excel at text generation but often produce hallucinations due to their sole reliance on parametric knowledge. |
| Approach: | They propose a framework that integrates anticipatory planning into legal text generation by generating keyphrases outlining future content serving as forward-looking plan. |
| Outcome: | The proposed framework improves factual accuracy and coherence by retrieving information aligned with the intended content. |
LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments (2025.acl-long)
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| Challenge: | Existing work on IR focus on retrieving entire cases rather than precise, paragraph-level information. |
| Approach: | They propose a cross-lingual dataset for paragraph-level retrieval from ECtHR judgments . they evaluate retrieval models in a zero-shot setting and use multilingual case law guides . |
| Outcome: | The proposed model excels in cross-lingual retrieval, while siamese architectures are better suited for monolingual tasks. |
LexTempus: Enhancing Temporal Generalizability of Legal Language Models Through Dynamic Mixture of Experts (2025.acl-long)
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| Challenge: | a rapid evolution of legal concepts requires that legal language models adapt swiftly accounting for the temporal dynamics. |
| Approach: | They propose a dynamic mixture of experts model that explicitly models the temporal evolution of legal language in an online learning framework. |
| Outcome: | The proposed model can model the temporal evolution of legal language without forgetting past knowledge. |