arXiv subjects

Arnab Bhattacharya

Publications and source records attributed to Arnab Bhattacharya.

At least 37 records · Page 2Linked to original sources

arXiv2025

IBPS: Indian Bail Prediction System

Bail decisions are among the most frequently adjudicated matters in Indian courts, yet they remain plagued by subjectivity, delays, and inconsistencies. With over 75% of India's prison population comprising undertrial prisoners, many from socioeconomically disadvantaged backgrounds, the lack of timely and fair bail adjudication exacerbates human rights concerns and contributes to systemic judicial backlog. In this paper, we present the Indian Bail Prediction System (IBPS), an AI-powered framework designed to assist in bail decision-making by predicting outcomes and generating legally sound rationales based solely on factual case attributes and statutory provisions. We curate and release a large-scale dataset of 150,430 High Court bail judgments, enriched with structured annotations such as age, health, criminal history, crime category, custody duration, statutes, and judicial reasoning. We fine-tune a large language model using parameter-efficient techniques and evaluate its performance across multiple configurations, with and without statutory context, and with RAG. Our results demonstrate that models fine-tuned with statutory knowledge significantly outperform baselines, achieving strong accuracy and explanation quality, and generalize well to a test set independently annotated by legal experts. IBPS offers a transparent, scalable, and reproducible solution to support data-driven legal assistance, reduce bail delays, and promote procedural fairness in the Indian judicial system.

cs.CL↗

arXiv2025

Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries

Legal precedent retrieval is a cornerstone of the common law system, governed by the principle of stare decisis, which demands consistency in judicial decisions. However, the growing complexity and volume of legal documents challenge traditional retrieval methods. TraceRetriever mirrors real-world legal search by operating with limited case information, extracting only rhetorically significant segments instead of requiring complete documents. Our pipeline integrates BM25, Vector Database, and Cross-Encoder models, combining initial results through Reciprocal Rank Fusion before final re-ranking. Rhetorical annotations are generated using a Hierarchical BiLSTM CRF classifier trained on Indian judgments. Evaluated on IL-PCR and COLIEE 2025 datasets, TraceRetriever addresses growing document volume challenges while aligning with practical search constraints, reliable and scalable foundation for precedent retrieval enhancing legal research when only partial case knowledge is available.

cs.CL↗

arXiv2025

Leveraging LLMs for Bangla Grammar Error Correction:Error Categorization, Synthetic Data, and Model Evaluation

Large Language Models (LLMs) perform exceedingly well in Natural Language Understanding (NLU) tasks for many languages including English. However, despite being the fifth most-spoken language globally, Grammatical Error Correction (GEC) in Bangla remains underdeveloped. In this work, we investigate how LLMs can be leveraged for improving Bangla GEC. For that, we first do an extensive categorization of 12 error classes in Bangla, and take a survey of native Bangla speakers to collect real-world errors. We next devise a rule-based noise injection method to create grammatically incorrect sentences corresponding to correct ones. The Vaiyakarana dataset, thus created, consists of 5,67,422 sentences of which 2,27,119 are erroneous. This dataset is then used to instruction-tune LLMs for the task of GEC in Bangla. Evaluations show that instruction-tuning with \name improves GEC performance of LLMs by 3-7 percentage points as compared to the zero-shot setting, and makes them achieve human-like performance in grammatical error identification. Humans, though, remain superior in error correction.

cs.CL↗

arXiv2025

A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs

Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across diverse tasks and languages. In this study, we focus on natural language understanding in three classical languages -- Sanskrit, Ancient Greek and Latin -- to investigate the factors affecting cross-lingual zero-shot generalization. First, we explore named entity recognition and machine translation into English. While LLMs perform equal to or better than fine-tuned baselines on out-of-domain data, smaller models often struggle, especially with niche or abstract entity types. In addition, we concentrate on Sanskrit by presenting a factoid question-answering (QA) dataset and show that incorporating context via retrieval-augmented generation approach significantly boosts performance. In contrast, we observe pronounced performance drops for smaller LLMs across these QA tasks. These results suggest model scale as an important factor influencing cross-lingual generalization. Assuming that models used such as GPT-4o and Llama-3.1 are not instruction fine-tuned on classical languages, our findings provide insights into how LLMs may generalize on these languages and their consequent utility in classical studies.

cs.CL↗

arXiv2025

BanglaByT5: Byte-Level Modelling for Bangla

Large language models (LLMs) have achieved remarkable success across various natural language processing tasks. However, most LLM models use traditional tokenizers like BPE and SentencePiece, which fail to capture the finer nuances of a morphologically rich language like Bangla (Bengali). In this work, we introduce BanglaByT5, the first byte-level encoder-decoder model explicitly tailored for Bangla. Built upon a small variant of Googles ByT5 architecture, BanglaByT5 is pre-trained on a 14GB curated corpus combining high-quality literary and newspaper articles. Through zeroshot and supervised evaluations across generative and classification tasks, BanglaByT5 demonstrates competitive performance, surpassing several multilingual and larger models. Our findings highlight the efficacy of byte-level modelling for morphologically rich languages and highlight BanglaByT5 potential as a lightweight yet powerful tool for Bangla NLP, particularly in both resource-constrained and scalable environments.

cs.CL↗

arXiv2025

Fermi surface nesting driven anomalous Hall effect in magnetically frustrated Mn_2PdIn

Noncollinear magnets with near-zero net magnetization and nontrivial bulk electronic topology hold significant promise for spintronic applications, though their scarcity necessitates purposeful design strategies. In this work, we report a topologically nontrivial electronic structure in metallic Mn_2PdIn, which crystallizes in the inverse Heusler structure and exhibits a spin-glassy ground state with quenched magnetization. The system features Weyl-type band crossings near the Fermi level and reveals a novel interplay among momentum-space nesting, orbital hybridization, and spin-orbit coupling. Comprehensive transport measurements uncover a pronounced anomalous Hall effect (AHE) in Mn_2PdIn. The observed quadratic relationship between the longitudinal and anomalous Hall resistivities highlights the intrinsic Berry curvature contribution to AHE. These findings establish inverse Heusler alloys as compelling platforms for realizing noncollinear magnets that host Weyl-type semimetallic or metallic phases-combining suppressed magnetization with robust electronic transport-thereby offering a promising route toward their seamless integration into next-generation spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

PARAMANU-GANITA: Can Small Math Language Models Rival with Large Language Models on Mathematical Reasoning?

In this paper, we study whether domain specific pretraining of small generative language models (SLM) from scratch with domain specialized tokenizer and Chain-of-Thought (CoT) instruction fine-tuning results in competitive performance on mathematical reasoning compared to LLMs? Secondly, whether this approach is environmentally sustainable, highly cost efficient? To address these research questions, we present Paramanu-Ganita, a 208 million-parameter novel decoder-only Auto Regressive SLM on mathematics. We performed pretraining from scratch on 31.5 billion tokens for 170 A100 hours using a context size of 4096 on a mixed mathematical corpus consisting of web pages, source code, textbooks, CoT templatised StackOverflow QA pairs, and mathematical lecture notes in LaTeX curated by us. We also trained a math and code specialised BPE tokenizer. We proposed and performed CoT instruction fine-tuning of Paramanu-Ganita on the MetaMathQA dataset. Our model Paramanu-Ganita, despite being 34 times smaller than the 7B LLMs, outperforms generalist LLMs by approximately 30% points, and even math-specialised LLMs by 3-23% points in GSM8K test accuracy metric. On MATH benchmark, Paramanu-Ganita outperformed the various models by 6-8% points. On benchmarks like LogiQA, MMLU (high school, college level), and competitive exams level, AGIEVAL (AQuA-RAT, SAT-Math), Paramanu-Ganita outperformed others by 1-4%. Our model is available at https://huggingface.co/gyanai/paramanu-ganita-208M-hf .

cs.CL↗

arXiv2025

LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification

In this paper, we address the task of semantic segmentation of legal documents through rhetorical role classification, with a focus on Indian legal judgments. We introduce LegalSeg, the largest annotated dataset for this task, comprising over 7,000 documents and 1.4 million sentences, labeled with 7 rhetorical roles. To benchmark performance, we evaluate multiple state-of-the-art models, including Hierarchical BiLSTM-CRF, TransformerOverInLegalBERT (ToInLegalBERT), Graph Neural Networks (GNNs), and Role-Aware Transformers, alongside an exploratory RhetoricLLaMA, an instruction-tuned large language model. Our results demonstrate that models incorporating broader context, structural relationships, and sequential sentence information outperform those relying solely on sentence-level features. Additionally, we conducted experiments using surrounding context and predicted or actual labels of neighboring sentences to assess their impact on classification accuracy. Despite these advancements, challenges persist in distinguishing between closely related roles and addressing class imbalance. Our work underscores the potential of advanced techniques for improving legal document understanding and sets a strong foundation for future research in legal NLP.

cs.CL↗

arXiv2025

Unconventional anomalous Hall effect in hexagonal polar magnet Y_3Co_8Sn_4

We report a rare realization of unconventional anomalous Hall effect (UAHE) both below and above the magnetic transition temperature (T_C) in a hexagonal noncentrosymmetric magnet Y_3Co_8Sn_4, using a combined experimental and ab-initio calculations. Occurrence of such UAHE is mainly attributed to the reciprocal (KS) topology (i.e. the presence of topological Weyl points at/near the Fermi level), along with some contribution from the topological magnetic texture, as inferred from the measured field-dependent ac susceptibility. The effect of UAHE on the measured transport behavior however evolves differently with temperature above and below T_C, suggesting different physical mechanism responsible in the two phases. A unique planar ferrimagnetic ordering is found to be the most stable state with ab-plane as the easy plane below TC, as observed experimentally. The simulated net magnetization and the moment per Co atom agrees fairly well with the measured values. A reasonably large AHC is also observed in both the phases (above and below and T_C) of the present compound, which is again not so ubiquitous. Our results underscore the family of R_3Co_8Sn_4 (R= rare earth) polar magnets as a compelling backdrop for exploring the synergy of topological magnetism and non-trivial electronic bands, pivotal for spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Semantically Cohesive Word Grouping in Indian Languages

Indian languages are inflectional and agglutinative and typically follow clause-free word order. The structure of sentences across most major Indian languages are similar when their dependency parse trees are considered. While some differences in the parsing structure occur due to peculiarities of a language or its preferred natural way of conveying meaning, several apparent differences are simply due to the granularity of representation of the smallest semantic unit of processing in a sentence. The semantic unit is typically a word, typographically separated by whitespaces. A single whitespace-separated word in one language may correspond to a group of words in another. Hence, grouping of words based on semantics helps unify the parsing structure of parallel sentences across languages and, in the process, morphology. In this work, we propose word grouping as a major preprocessing step for any computational or linguistic processing of sentences for Indian languages. Among Indian languages, since Hindi is one of the least agglutinative, we expect it to benefit the most from word-grouping. Hence, in this paper, we focus on Hindi to study the effects of grouping. We perform quantitative assessment of our proposal with an intrinsic method that perturbs sentences by shuffling words as well as an extrinsic evaluation that verifies the importance of word grouping for the task of Machine Translation (MT) using decomposed prompting. We also qualitatively analyze certain aspects of the syntactic structure of sentences. Our experiments and analyses show that the proposed grouping technique brings uniformity in the syntactic structures, as well as aids underlying NLP tasks.

cs.CL↗

arXiv2024

Giant Topological Hall Effect in Magnetic Weyl Metal Mn$_{2}$Pd$_{0.5}$Ir$_{0.5}$Sn

The synergy between real and reciprocal space topology is anticipated to yield a diverse array of topological properties in quantum materials. We address this pursuit by achieving topologically safeguarded magnetic order in novel Weyl metallic Heusler alloy, Mn$_{2}$Pd$_{0.5}$Ir$_{0.5}$Sn. The system possesses non-centrosymmetric D$_{2d}$ crystal symmetry with notable spin-orbit coupling effects. Our first principles calculations confirm the topological non-trivial nature of band structure, including 42 pairs of Weyl nodes at/near the Fermi level, offering deeper insights into the observed anomalous Hall effect mediated by intrinsic Berry curvature. A unique canted magnetic ordering facilitates such rich topological features, manifesting through an exceptionally large topological Hall effect at low fields. The latter is sustained even at room temperature and compared with other known topological magnetic materials. Detailed micromagnetic simulations demonstrate the possible existence of an antiskyrmion lattice. Our results underscore the $D_{2d}$ Heusler magnets as a possible platform to explore the intricate interplay of non-trivial topology across real and reciprocal spaces to leverage a plethora of emergent properties for spintronic applications.

cond-mat.mtrl-sci↗

arXiv2024

NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis

The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system. This paper introduces NyayaAnumana, the largest and most diverse corpus of Indian legal cases compiled for LJP, encompassing a total of 7,02,945 preprocessed cases. NyayaAnumana, which combines the words "Nyay" (judgment) and "Anuman" (prediction or inference) respectively for most major Indian languages, includes a wide range of cases from the Supreme Court, High Courts, Tribunal Courts, District Courts, and Daily Orders and, thus, provides unparalleled diversity and coverage. Our dataset surpasses existing datasets like PredEx and ILDC, offering a comprehensive foundation for advanced AI research in the legal domain. In addition to the dataset, we present INLegalLlama, a domain-specific generative large language model (LLM) tailored to the intricacies of the Indian legal system. It is developed through a two-phase training approach over a base LLaMa model. First, Indian legal documents are injected using continual pretraining. Second, task-specific supervised finetuning is done. This method allows the model to achieve a deeper understanding of legal contexts. Our experiments demonstrate that incorporating diverse court data significantly boosts model accuracy, achieving approximately 90% F1-score in prediction tasks. INLegalLlama not only improves prediction accuracy but also offers comprehensible explanations, addressing the need for explainability in AI-assisted legal decisions.

cs.CL↗

arXiv2024

Large anomalous Hall effect and \textit{A}-phase in hexagonal polar magnet Gd$_3$Ni$_8$Sn$_4$

While recent theoretical studies have positioned noncollinear polar magnets with $C_{nv}$ symmetry as compelling candidates for realizing topological magnetic phases and substantial intrinsic anomalous Hall conductivity, experimental realizations of the same in strongly correlated systems remain rare. Here, we present a large intrinsic anomalous Hall effect and extended topological magnetic ordering in Gd$_3$Ni$_8$Sn$_4$ with hexagonal $C_{6v}$ symmetry. Observation of topological Hall response, corroborated by metamagnetic anomalies in isothermal magnetization, peak/hump features in field-evolution of ac susceptibility and longitudinal resistivity, attests to the stabilization of skyrmion $A$-phase. The anomalous Hall effect is quantitatively accounted for by the intrinsic Berry curvature-mediated mechanism. Our results underscore polar magnets as a promising platform to investigate a plethora of emergent electrodynamic responses rooted in the interplay between magnetism and topology.

cond-mat.str-el↗

arXiv2024

Rethinking Legal Judgement Prediction in a Realistic Scenario in the Era of Large Language Models

This study investigates judgment prediction in a realistic scenario within the context of Indian judgments, utilizing a range of transformer-based models, including InLegalBERT, BERT, and XLNet, alongside LLMs such as Llama-2 and GPT-3.5 Turbo. In this realistic scenario, we simulate how judgments are predicted at the point when a case is presented for a decision in court, using only the information available at that time, such as the facts of the case, statutes, precedents, and arguments. This approach mimics real-world conditions, where decisions must be made without the benefit of hindsight, unlike retrospective analyses often found in previous studies. For transformer models, we experiment with hierarchical transformers and the summarization of judgment facts to optimize input for these models. Our experiments with LLMs reveal that GPT-3.5 Turbo excels in realistic scenarios, demonstrating robust performance in judgment prediction. Furthermore, incorporating additional legal information, such as statutes and precedents, significantly improves the outcome of the prediction task. The LLMs also provide explanations for their predictions. To evaluate the quality of these predictions and explanations, we introduce two human evaluation metrics: Clarity and Linking. Our findings from both automatic and human evaluations indicate that, despite advancements in LLMs, they are yet to achieve expert-level performance in judgment prediction and explanation tasks.

cs.CL↗

arXiv2024

RSVP: Beyond Weisfeiler Lehman Graph Isomorphism Test

Graph isomorphism, a classical algorithmic problem, determines whether two input graphs are structurally identical or not. Interestingly, it is one of the few problems that is not yet known to belong to either the P or NP-complete complexity classes. As such, intelligent search-space pruning based strategies were proposed for developing isomorphism testing solvers like nauty and bliss, which are still, unfortunately, exponential in the worst-case scenario. Thus, the polynomial-time Weisfeiler-Lehman (WL) isomorphism testing heuristic, based on colour refinement, has been widely adopted in the literature. However, WL fails for multiple classes of non-isomorphic graph instances such as strongly regular graphs, block structures, and switched edges, among others. In this paper, we propose a novel polynomial-time graph isomorphism testing heuristic, RSVP, and depict its enhanced discriminative power compared to the Weisfeiler-Lehman approach for several challenging classes of graphs. Bounded by a run-time complexity of O(m^2+mn^2+n^3) (where n and m are the number of vertices and edges respectively), we show that RSVP can identify non-isomorphism in several 'hard' graph instance classes including Miyazaki, Paulus, cubic hypohamiltonian, strongly regular, Latin series and Steiner triple system graphs, where the 3-WL test fails. Similar to the WL test, our proposed algorithm is prone to only one-sided errors, where isomorphic graphs will never be determined to be non-isomorphic, although the reverse can happen.

cs.DS↗

arXiv2024

Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts

In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant challenges due to the complexity of legal proceedings and the scarcity of expert-annotated datasets. Addressing this, we introduce \textbf{Pred}iction with \textbf{Ex}planation (\texttt{PredEx}), the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context, featuring over 15,000 annotations. This groundbreaking corpus significantly enhances the training and evaluation of AI models in legal analysis, with innovations including the application of instruction tuning to LLMs. This method has markedly improved the predictive accuracy and explanatory depth of these models for legal judgments. We employed various transformer-based models, tailored for both general and Indian legal contexts. Through rigorous lexical, semantic, and expert assessments, our models effectively leverage \texttt{PredEx} to provide precise predictions and meaningful explanations, establishing it as a valuable benchmark for both the legal profession and the NLP community.

cs.CL↗

arXiv2024

HLDC: Hindi Legal Documents Corpus

Many populous countries including India are burdened with a considerable backlog of legal cases. Development of automated systems that could process legal documents and augment legal practitioners can mitigate this. However, there is a dearth of high-quality corpora that is needed to develop such data-driven systems. The problem gets even more pronounced in the case of low resource languages such as Hindi. In this resource paper, we introduce the Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents in Hindi. Documents are cleaned and structured to enable the development of downstream applications. Further, as a use-case for the corpus, we introduce the task of bail prediction. We experiment with a battery of models and propose a Multi-Task Learning (MTL) based model for the same. MTL models use summarization as an auxiliary task along with bail prediction as the main task. Experiments with different models are indicative of the need for further research in this area. We release the corpus and model implementation code with this paper: https://github.com/Exploration-Lab/HLDC

cs.CL↗

arXiv2024

Spin-Valve-Like Magnetoresistance and Anomalous Hall Effect in Magnetic Weyl Metal Mn$_2$PdSn

Realization of noncentrosymmetric magnetic Weyl metals is expected to exhibit anomalous transport properties stemming from the interplay of unusual bulk electronic topology and magnetism. Here, we present spin-valve-like magnetoresistance at room temperature in ferrimagneticWeyl metal Mn$_2$PdSn that crystallizes in the inverse Heusler structure. Anomalous magnetoresistance display dominant asymmetric component attributed to domain wall electron scattering, indicative of spin-valve-like behavior. Ab initio calculations confirm the topologically non-trivial nature of the band structure, with three pairs of Weyl nodes proximate to the Fermi level, providing deeper insights into the observed intrinsic Berry curvature mediated substantial anomalous Hall conductivity. Our results underscore the inverse Heusler compounds as promising platform to realize magnetic Weyl metals/semimetals and leverage emergent transport properties for electronic functionalities.

cond-mat.mtrl-sci↗