The semantic gap between informal user queries

The gap

Evidence profile

Sourced from the future work and stated research gap of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 4 representative gaps

• IMLJD: A Computational Dataset for Indian Matrimonial Litigation Analysis (2026) · arXiv

  IMLJD provides the first structured, labelled, and reproducible computational dataset for Indian matrimonial litigation, built from public judicial archives. The dataset surfaces a 19.6-point quash success rate differential between SC and HC levels on a matched time period, a 15% settlement rate at the quash petition stage, and year-level trends in litigation volume. All data, code, and the knowledge graph are released openly. The pipeline is designed for extension to additional High Courts and future years. Researchers using this dataset should note that quash success rates do not indicate the veracity of underlying allegations. A quashed FIR reflects a procedural determination, not a factual finding. The metadata-derived indicators are descriptive starting points for hypothesis generation, not validated classifiers. Downstream applications that use this dataset to argue that matrimonial complaints are systematically false would misrepresent both the data and the legal process. Future work includes OCR-based extraction of full-text Supreme Court judgments, extension to additional High Courts, rhetorical-role labelling, and citation-outcome analysis. Full-text extraction would enable deeper analysis of judicial reasoning patterns, including procedural grounds for dismissal, maintenance rejection, and settlement-linked quashing. Integration with APIs such as Indian Kanoon or improved OCR pipelines may support richer precedent analysis and temporal litigation pathways.

  generalfuture work
  Keywords: dataset litigation quash indian matrimonial judicial success rate settlement extension additional high courts future procedural
• JustiFind: An Intelligent Legal Aid and Awareness System (2026) · International Research Journal of Modernization in Engineering Technology & Science · doi

  VII. Support for Multilingual and Voice First • • Fine-Tuning Legal LLM • Case tracking and personalized legal • Mobile app and off-line capabilities • Integration with government/ NGO ecosystem VIII. REFERENCES S. Gupta, R. Mehta, and P. Agarwal, "AI in Legal Domain: Semantic Understanding of Law Documents," in Proc. IEEE Conference on Computational Linguistics, 2021. P. Ramesh and S. Kulkarni, "Building Conversational Legal Chatbots Using Machine Learning," IEEE Transactions on Computational Social Systems, 2022. A. Kumar and V. Narayan, "Semantic Search Techniques for Legal Information Retrieval," Springer International Conference on Data Engineering and Applications, 2020. N. Desai and R. Sinha, "Bridging Legal Awareness through AI: Opportunities and Challenges," International Journal of AI Applications and Innovation, vol. 14, no. 2, pp. 45–62, 2023. C. Trivedi, S. Kumar, I. Mohd, R. Bhalla, N. A. Lone, and D. Dogra, "Leveraging AI-Driven Chatbots for Legal Literacy," IEEE Access, vol. 12, pp. 78213–78229, 2024. Nikita, E. Srivastav, A. Patel, A. Singh, R. Sharma, D. P. Rana, and R. G. Mehta, "LAWBOT: A Smart User Indian Legal Chatbot using Machine Learning Framework," in Proc. International Conference on Emerging Technologies in Computing, 2024. K. D. Ashley, Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press, 2017. M. Medvedeva, M. Vols, and M. Wieling, "Using Machine Learning to Predict Decisions of the European Court of Human Rights," Artificial Intelligence and Law, vol. 28, no. 2, pp. 237–266, 2020. E. S. Kamarudin and M. Ismail, "Promoting Civic Engagement through Digital Platforms: The Case for Legal Awareness," International Journal of Law and Information Technology, vol. 28, no. 3, pp. 201– 224, 2020. S. P. Smith, "Combating Legal Misinformation: The Role of Technology in Public Education," Journal of Legal Communication and Rhetoric, vol. 17, no. 1, pp. 89–112, 2020. N. Reimers and I. Gurevych, "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks," in Proc. EMNLP 2019, 2019. Meta AI, "LLaMA: Open and Efficient Foundation Language Models," arXiv preprint arXiv:2302.13971, 2023.

  generalfuture workevidence 5/5
  Keywords: legal international using journal proc ieee conference machine learning technology case mehta semantic computational chatbots
• Bridging Legal Language Barriers Using Explainable AI: Outcome Prediction and Multilingual Knowledge based answer retrieval for Indian Law (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · doi

  The paper proposes an integrated legal AI system that incorporates case outcome prediction and legal question answering. This shows that transparent models are capable of competitive performance (91.3% accuracy) without sacrificing interpretability. On 26,688 Indian Supreme Court cases, logistic regression with confidence calibration has a higher performance (r = 0.73) by 9.3 percentage points over existing work. RAG-based legal question answering has 86% correctness with 7% hallucinations, which is 70% less than existing baseline LLMs. The work is shown to be accessible to non-expert citizens with 4.0/5 user satisfaction across three languages: English, Hindi, and Tamil, along with interactive explainability visualizations. Key Takeaways: (1) Interpretability in ML can be a viable approach to high-stakes legal prediction with careful feature engineering and confidence calibration. (2) RAG architecture can significantly reduce LLM hallucinations in domain-specific settings. (3) Transparent AI systems with confidence gauges, feature importance, and similar case retrieval can achieve 92% professional comprehension for trustworthy deployment. (4) Achieving 79% to 82% translation approval for multilingual accessibility also proves the feasibility of equitable legal AI in India’s linguistic diversity. User evaluation with 35 participants, consisting of 15 law students, 12 legal professionals, and 8 general users, recorded an overall satisfaction score of 4.0/5. The most valuable features, as rated by users, are multilingual support at 4.5/5, explainability visualizations at 4.3/5, prediction accuracy at 4.1/5, and answer quality at 3.9/5. Comparison of expert ratings with existing legal AI tools shows that the proposed system has the highest Indian law specificity with a favorable balance between clarity and completeness.

  generalfuture workevidence 5/5
  Keywords: legal prediction confidence existing system case question answering shows transparent performance accuracy interpretability indian calibration
• SC-HyDE: Mitigating Hallucinations in Chinese Legal Question Answering via Self-Corrected Hypothetical Document Embeddings (2026) · Mathematical Modeling and Algorithm Application · doi

  The semantic gap between informal user queries and professional legal terminology is a challenge in applying RAG in the Chinese legal landscape. The direct application of RAG to the Chinese legal domain is unique and gravely problematic. The long-tail nature of the distribution of statutes is a challenge in developing effective legal question answering systems.

  generalstated research gapevidence 5/5
  Keywords: semantic gap between informal user queries professional legal

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