arXiv subjects

Basit Ali

Publications and source records attributed to Basit Ali.

4 recordsLinked to original sources

arXiv2026

Revealing Dislocation Interactions Controlling Mechanical Properties of Metals

During plastic deformation, metals change shape while continuously becoming stronger. The microscopic origin of these processes lies in the proliferation and movement of line defects, dislocations, and the subsequent self-organisation and pinning of dislocations on lattice imperfections, including other dislocations. The nature of these multiscale processes has remained elusive because in situ observations have not been feasible. We present 3D movies of how dislocations pile up near an obstacle, deeply within a mm-sized pure Al sample and during tensile deformation. Cross-slip is found to provide a mechanism for the dislocations to escape the pile-up, leading to pronounced intermittent behaviour. Such data support a new generation of dislocation dynamics and micro-mechanics modelling.

cond-mat.mtrl-sci↗

arXiv2025

Explainable Statute Prediction via Attention-based Model and LLM Prompting

In this paper, we explore the problem of automatic statute prediction where for a given case description, a subset of relevant statutes are to be predicted. Here, the term "statute" refers to a section, a sub-section, or an article of any specific Act. Addressing this problem would be useful in several applications such as AI-assistant for lawyers and legal question answering system. For better user acceptance of such Legal AI systems, we believe the predictions should also be accompanied by human understandable explanations. We propose two techniques for addressing this problem of statute prediction with explanations -- (i) AoS (Attention-over-Sentences) which uses attention over sentences in a case description to predict statutes relevant for it and (ii) LLMPrompt which prompts an LLM to predict as well as explain relevance of a certain statute. AoS uses smaller language models, specifically sentence transformers and is trained in a supervised manner whereas LLMPrompt uses larger language models in a zero-shot manner and explores both standard as well as Chain-of-Thought (CoT) prompting techniques. Both these models produce explanations for their predictions in human understandable forms. We compare statute prediction performance of both the proposed techniques with each other as well as with a set of competent baselines, across two popular datasets. Also, we evaluate the quality of the generated explanations through an automated counter-factual manner as well as through human evaluation.

cs.CL↗

arXiv2025

DRAssist: Dispute Resolution Assistance using Large Language Models

Disputes between two parties occur in almost all domains such as taxation, insurance, banking, healthcare, etc. The disputes are generally resolved in a specific forum (e.g., consumer court) where facts are presented, points of disagreement are discussed, arguments as well as specific demands of the parties are heard, and finally a human judge resolves the dispute by often favouring one of the two parties. In this paper, we explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes, as part of our DRAssist system. We focus on disputes from two specific domains -- automobile insurance and domain name disputes. DRAssist identifies certain key structural elements (e.g., facts, aspects or disagreement, arguments) of the disputes and summarizes the unstructured dispute descriptions to produce a structured summary for each dispute. We then explore multiple prompting strategies with multiple LLMs for their ability to assist in resolving the disputes in these domains. In DRAssist, these LLMs are prompted to produce the resolution output at three different levels -- (i) identifying an overall stronger party in a dispute, (ii) decide whether each specific demand of each contesting party can be accepted or not, (iii) evaluate whether each argument by each contesting party is strong or weak. We evaluate the performance of LLMs on all these tasks by comparing them with relevant baselines using suitable evaluation metrics.

cs.CL↗

arXiv2024

Exploring Charged Higgs at the Future Circular Collider (FCC): A Review of Two-Higgs-Doublet Models

This paper reports on the theoretical investigation of charged Higgs bosons and their coupling to fermions within the Two Higgs Doublet Model (THDM). The study focuses on the discovery potential of charged Higgs bosons predicted in Types III and IV at the future Circular Hadron-Hadron Collider (FCC-hh) with a center-of-mass energy of (\sqrt{s} = 100) TeV. By analyzing their decays, couplings to fermions, branching ratios, and production cross-section via (pp \rightarrow tH^-), we investigate the signatures of charged Higgs bosons, including kinematical distributions based on the background processes of (b\bar{b}) quarks.

hep-ph↗