A systematic evaluation framework for BIM-QA systems comprising a four-category question taxonomy based on data retrieval complexity, and an evaluation protocol with five binary quality criteria, enabling consistent benchmarking and targeted diagnosis of system weaknesses is presented.
Eagle, a comprehensive benchmarking framework tailored for evaluating OpsLLMs, delivers a deployable foundation for advancing large-model applications in AIOps and open-source the framework and dataset to foster community adoption and reproducibility.
Yuhe Liu, Changhua Pei, Hang Wang et al.· SIGSOFT FSE Companion· 0 citations
This work proposes a comprehensive pipeline for improving financial QA systems through high-quality synthetic data generation and fine-tuning of smaller language models (SLMs) using Quantized Low-Rank Adaptation (QLoRA).
Lokendra Birla, Milind Savagaonkar, Visnu Srinivasan et al.· 0 citations
Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requirements for both answer quality and efficiency in high-risk scenarios. To address this issue, this paper proposes CoAL-RAG, a complexity-aware legal retrieval-augmented generation method, which constructs a multi-dimensional evaluation mechanism based on ``question essence''and ``retrieval consistency''to enable adaptive routing of retrieval strategies. First, the reasoning demand is quantified according to the logical structure of the question. Then, the discrepancy between semantic retrieval and keyword retrieval is utilized to indirectly reflect problem complexity, thereby selecting the most appropriate retrieval strategy and dynamically filtering contextual information. Experimental results demonstrate that the proposed method significantly outperforms baseline models not only on Chinese legal benchmarks (SocialLawQA, LawBench) but also demonstrates strong cross-jurisdictional generalization on English datasets (LexGLUE, CaseHold). Specifically, on Chinese datasets, the BLEU score improves by 42.5\% and ROUGE-L reaches 3.6 times that of knowledge graph-based methods. On English benchmarks, CoAL-RAG maintains highly competitive accuracy, achieving an optimal balance between generation quality, deep logical reasoning, and system efficiency across different legal systems.
Jinyue Su, Zhuofeng Zhao, Huanhuan Wang et al.· 0 citations
Evaluating the quality of long-form answers generated by Question Answering systems presents significant challenges. Traditional metrics, such as BLEU and ROUGE, often reduce the assessment to a single similarity score with a reference answer, failing to capture semantic and specific aspects of answer quality. This reliance on an aggregated score not only overlooks important dimensions but also depends heavily on the availability of reference answers, which may not always be practical or sufficient. Developing metrics capable of individually assessing specific criteria, particularly completeness and relevance, is crucial for identifying weaknesses and guiding improvements in these systems. To address these limitations, this paper introduces specialized metrics designed to evaluate completeness and relevance of long answers without the need for reference texts. We present a new dataset comprising long answers to instructional questions in Computer Science, annotated by human experts based on completeness and relevance. Building upon this, we propose three novel metric models: (1) a prompt-based strategy utilizing Large Language Models to assess answers, (2) an approach that adapts precision and recall concepts by segmenting answers into discrete information units, and (3) a regression model trained on synthetic data to predict completeness and relevance scores. Experimental results demonstrate that the proposed metrics closely align with human judgments and provide more detailed evaluations of completeness and relevance compared to traditional metrics. By enabling a more granular assessment, these metrics facilitate targeted refinements in QA systems, enhancing their ability to meet users’ informational needs more effectively.
E. Cortes, Renata Vieira, D. Barone· Language Resources and Evalu...· 0 citations
We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA). Quanta evaluates multimodal quizbowl systems that answer pyramid-style questions from incrementally revealed text and accompanying images while operating under realistic efficiency constraints. The challenge consists of two distinct tasks: Tossup questions, which require deciding when to answer under uncertainty, and Bonus questions, which emphasize accurate answer selection and human adoption. To address these differing objectives, we develop a task-specific two-agent architecture. Our Tossup agent utilizes a GPT-4o-mini-class model (referred to as GPT-4.1-mini in the competition logs) with confidence-calibrated answering and a domain-specific numeric reasoning policy that reduces overconfident predictions from isolated quantitative clues. Our Bonus agent uses GPT-4o-class model (referred to as GPT-4.1) with leadin-aware reasoning, structured relational reasoning, and multimodal evidence integration to improve exact answer selection. Rather than relying on a retrieval pipeline or model ensembles, our approach emphasizes efficient reasoning policies and confidence calibration within a hosted-only environment. Our system achieved the highest overall leaderboard score of 0.402, including a Tossup score of 0.238 and a Bonus Effect score of 0.164. The results demonstrate that lightweight, task-specific reasoning strategies can provide strong performance on resource-constrained multimodal question answering benchmarks.
This study develops a multi-source Retrieval-Augmented Generation (RAG) based Question Answering (QA) system that automatically integrates heterogeneous knowledge sources through a unified source parameter to enhance knowledge transfer and question answering for organizational support and employee onboarding.
Krisna Dwi Setya Adi, Ivan Michael Siregar· Jurnal Ragam Pengabdian· 0 citations
