AIQB
ResearchOrdinary

ViLegalExpert: A Large-Scale Benchmark for Vietnamese Legal Retrieval and Question Answering from Real-World Consultations

Source: arXiv·

Summary

Trustworthy Legal AI requires systems that can answer legal questions while grounding their responses in authoritative sources. However, existing Vietnamese legal benchmarks provide limited coverage of real-world legal consultations. We introduce \textbf{ViLegalExpert}, a large-scale benchmark constructed from authentic citizen--lawyer consultations, containing over \textbf{172K} questions across \textbf{34 legal domains}, together with professional answers and expert-verified legal evidence. ViLegalExpert supports legal information retrieval, extractive QA, and abstractive QA. Experiments with representative retrieval methods and language models reveal substantial challenges in evidence retrieval and grounded answer generation. While pretrained models perform strongly on QA, hybrid retrieval achieves the best retrieval performance. These results demonstrate the difficulty of mapping naturally expressed legal questions to authoritative provisions and establish ViLegalExpert as a challenging benchmark for reliable Vietnamese Legal AI.
CategoryAI Research & Papers
TierOrdinary
Published
Indexed by AIQB
SourcearXiv
AIQB record IDintel-691104fb9445c92df5e37e66
Read original source ↗View data sourceMore Research

Related stories

Matched by category, entity tags and source

ResearchOrdinary
In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works from the recent literature propose streaming anomaly detection…
Source: arXiv
ResearchOrdinary
Minimax Additive Regression under Unknown Dependent Designs
We study additive regression under a potentially non-product random design on $[0,1]^d$, allowing the dimension $d$ to grow with the sample size $n$. We introduce coupled smoothness classes that separately control the regularity of the mar…
Source: arXiv
ResearchOrdinary
UniAE-MoE: A Unified Audio Encoder via Mixture of Experts
Large Audio Language Models (LALMs) rely on effective audio encoders for multi-task performance. We introduce UniAE-MoE, a unified audio encoder designed to model cross-domain audio representations and achieve outstanding downstream unders…
Source: arXiv · Qwen
ResearchOrdinary
Importance-Aware Feature Sparsification for Wireless Split Learning
Wireless split learning (SL) reduces on-device computation by offloading upper layers to a server, yet transmitting high-dimensional intermediate features at each iteration remains a major communication bottleneck. Existing methods select …
Source: arXiv
ResearchOrdinary
Dynamics to decision: A mathematical theory of Lyapunov spectra and decision boundaries in deep classifiers
A deep classifier is defined not only by the decision it produces, but also by the sequence of transformations through which that decision is formed. Treating this evolution as a dynamical system across layers provides a natural framework …
Source: arXiv
ResearchOrdinary
Attention Function as an Intrinsic Inductive Bias: How Models' Behavior Diverges in Novel Contexts
Developmental psychology holds that certain priors are given to infants prior to experience rather than induced from data, and that the influence of such priors is suppressed under strong, well-constrained conditions but reasserts itself u…
Source: arXiv · GPT
ResearchOrdinary
Low-Discrepancy Dither for Quantized Recurrent State Caches
Mamba-style and hybrid language models compress their past into a fixed-size recurrent state that is rewritten at every generated token. Storing this state in low precision saves memory bandwidth, but every rounding error is fed back into …
Source: arXiv
ResearchOrdinary
Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers
Fiber orientation and compartmental microstructure are central to the characterization of white matter tissue in diffusion MRI, yet existing methods either resolve fiber orientations without quantifying microstructure, or quantify microstr…
Source: arXiv
ResearchOrdinary
MEND: Label-Free Detection, Localisation, and Correction of Latent Hallucination in World Models
World Models are appearing as the next major frontier in computer vision. However, their robustness is currently largely unexplored. We identify the phenomenon of hallucination in latent World Models: given a state and an action, the predi…
Source: arXiv
ResearchOrdinary
Whitening Improves Robustness to Spurious Correlations in Linear Probes
Deep neural networks tend to rely on simple features that may be spurious and thus fail to generalize. We study this problem in the setting of linear probes, where a (generalized) linear model is fitted on the representations of a (pretrai…
Source: arXiv