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This paper introduces AILQA, an AI-driven system for legal question answering in the Indian legal context, using embedding and generative models with retrieval-augmented generation (RAG). It evaluates performance on Indian legal texts and the All India Bar Exam, finding that AI-generated responses can sometimes outperform reference answers.
Presents FiT, a diagnostic framework to evaluate small LLMs on cybersecurity QA capabilities before fine-tuning, showing that fine-tuning can degrade vocabulary and parametric knowledge depending on the regime. Provides guidance to avoid unnecessary fine-tuning.
Debate-on-Graph (DoG) is a framework that enhances LLM reasoning by leveraging uncertain knowledge graphs (UKGs) with confidence scores, using a heuristic search and multi-agent debate mechanism to produce reliable answers. It achieves state-of-the-art performance on four QA benchmarks.
This paper introduces FinanceComplexQA, a comprehensive benchmark for evaluating agentic reasoning on industrial-grade financial documents, featuring bilingual support, expert-level questions, and complex layouts across six scenarios and seven tasks.
Introduces LakeQuest, a human-validated benchmark of 9,846 QA pairs across three domains for evaluating end-to-end retrieve-and-synthesize pipelines over heterogeneous data lakes, revealing critical failure modes in modern QA systems.
Introduces CANDI-QA, a dataset to evaluate LLMs on contextual alignment in niche domains, comprising information assistance and applied inference questions. Systematic evaluations across ten LLMs show challenges, and a lightweight neuro-symbolic framework MTSS-Net is proposed as baseline.
ResearchQA is a new benchmark of 6,211 single-paper question-answer pairs from 494 open-access papers across eight domains, designed to evaluate citation-grounded question-answering by requiring verifiable citations and supporting grounded refusal when evidence is insufficient.
CLIR-Bench is a benchmark for multimodal question answering over irregularly sampled clinical time series, constructed from ICU records with 6,600 QA instances across 11 clinical variables. It reveals that existing generalist models struggle with sparse temporal evidence, highlighting the need for stronger irregular time-series reasoning methods.
PolyUQuest is a verifiable, structure-aware web RAG framework that uses heterogeneous graphs to unify hyperlink topology, DOM hierarchy, and entity-relation knowledge, outperforming existing systems on answer correctness, coverage, and faithfulness.
This paper presents the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation (DA-MIVQA), which extends previous benchmarks with difficulty-aware annotations and three tracks: temporal answer grounding, video corpus retrieval, and grounding in corpus. The dataset includes medical instructional videos from public channels and aims to evaluate systems under varying reasoning requirements.
This paper presents an empirical study evaluating RAG evaluation metrics from four libraries (Ragas, DeepEval, RAGChecker, Opik) by comparing them to human judgments and standard recall metrics, using a question-answering dataset created from business data.
This paper extends PubHealthBench into a retrieval-augmented setting and evaluates retrieval and generation choices for public health QA, showing hybrid retrieval improves accuracy and that smaller models with retrieval can match larger ones.
This paper introduces RSF-GLLM, a framework that decouples differentiable graph reasoning from LLM generation to address the semantic gap in multi-hop knowledge graph question answering, achieving competitive performance with superior inference efficiency.
Introduces MultAttnAttrib, a training-free method for multimodal attribution in long document QA, along with the MultAttrEval benchmark. It outperforms prompting-based methods and matches frontier models like GPT-5.4.
Introduces MetaFlow, a method that trains large language models to generate zero-shot workflows for tasks by combining supervised fine-tuning and reinforcement learning with execution feedback, achieving strong generalization to untrained tasks and operator sets.
This paper introduces MamaBench and MamaRetrieval, two benchmarks for evaluating medical retrieval-augmented generation in maternal, neonatal, and reproductive health, addressing gaps in existing QA and retrieval datasets.
This paper proposes TriageRA-CCF, a method for adaptive rank budgeting in LoRA for medical question answering. It uses source-side signals (base-model confidence, clinical coverage, counterfactual proxy) to dynamically choose rank budgets, achieving modest accuracy gains on Qwen3-8B and Llama3.1-8B.
AB-RAG is a training-free, backbone-agnostic framework that adaptively retrieves passages for question answering by estimating answer confidence, improving efficiency and accuracy across multiple backbones and datasets.
PASTA is a novel framework for knowledge updating in LLMs that combines data augmentation, question-answering generation, and self-learning DPO to integrate factual information from news articles, achieving accuracy improvement from 0.02 to 0.82 while preserving general capabilities.
Proposes OPI, an ontology-guided framework for multi-hop knowledge graph question answering that leverages a relation-centric ontology graph for bidirectional retrieval and iterative refinement, achieving state-of-the-art results on multiple benchmarks.