K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation frameworks for large language models for domain specific tasks are coarse and do not provide a multidimensional evaluation of a model's ability to interpret domain specific data. |
| Approach: | They propose a diagnostic benchmark grounded in national qualification exams that exposes critical gaps across four dimensions: expert visual reasoning of charts, logical validity via expert-verified rationales, Korean-specific geo-cultural comprehension, and fine-grained domain analysis. |
| Outcome: | The proposed model outperforms global models in local contexts, demonstrating that parameter scaling alone cannot resolve cultural dependencies. |
Similar Papers
LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models (2026.acl-long)
Copied to clipboard
Jian Gao, Richeng Xuan, Zhaolu Kang, Dingshi Liao, Wenxin Huang, Zongmou Huang, Yangdi Xu, Bowen Qin, Zheqi He, Xi Yang, null Changjinli, Yonghua Lin
| Challenge: | Existing SEA-focused benchmarks miss Lao-specific cultural grounding and linguistic properties. |
| Approach: | They propose a multi-dimensional benchmark for assessing large language models in Lao . they use open-source and held-out subsets to evaluate languages with a hybrid pipeline . |
| Outcome: | LaoBench is the first large-scale, high-quality, and multidimensional benchmark for assessing LLM language understanding and reasoning in Lao. |
ScholarBench: A Bilingual Benchmark for Abstraction, Comprehension, and Reasoning Evaluation in Academic Contexts (2025.findings-emnlp)
Copied to clipboard
| Challenge: | ScholarBench evaluates domain-specific knowledge of large language models (LLMs) prior benchmarks lack the scalability to handle complex academic tasks. |
| Approach: | ScholarBench evaluates the academic reasoning ability of large language models . the benchmark is constructed through a three-step process . |
| Outcome: | ScholarBench evaluates the academic reasoning ability of large language models . the benchmark comprises 5,031 examples in Korean and 5,309 examples in English . |
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Grasping the concept of time is a fundamental facet of human cognition. |
| Approach: | They propose a hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal phenomena. |
| Outcome: | The proposed benchmark shows that state-of-the-art LLMs are still far behind humans in temporal reasoning . |
C²RBench: A Chinese Complex Reasoning Benchmark for Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks often fail to capture complex multi-step reasoning demands inherent in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate multi-step, multimodal advanced reasoning of large language models. |
| Outcome: | The proposed benchmark exceeds existing benchmarks in cognitive complexity and accuracy by over 90% . it features 1,115 carefully curated Chinese tasks organized into eight domain-specific subsets . evaluations of 20 LLMs and 24 multimodal large language models reveal critical performance gaps . |
AnalystBench: Benchmarking professional long-form report generation with web-mined multimodal tasks (2026.findings-acl)
Copied to clipboard
Chau Minh Pham, Zichao Wang, Puneet Mathur, Alexa Siu, Akriti Jain, Aparna Garimella, Ananya B. Sai, Nedim Lipka, Mohit Iyyer, Varun Manjunatha
| Challenge: | Existing benchmarks decompose the end-to-end professional report generation into individual components. |
| Approach: | They propose a benchmarking tool that evaluates 20 real-world professional report generation tasks grounded in multimodal document collections. |
| Outcome: | The proposed model outperforms closed-source models on executive summarization tasks but drops significantly on long-horizon synthesis tasks. |
LiveCultureBench: a Multi-Agent, Multi-Cultural Benchmark for Large Language Models in Dynamic Social Simulations (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly deployed as autonomous agents . evaluations focus primarily on task success rather than cultural appropriateness or reliability. |
| Approach: | They propose a multi-cultural, dynamic benchmark that embeds large language models as agents in a simulated town and evaluates them on task completion and adherence to socio-cultural norms. |
| Outcome: | The proposed model evaluates LLMs on task completion and adherence to socio-cultural norms across models and cultural profiles. |
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) and Retrieval-augmented Generation (RAG) systems show promise, but their performance on cross-document MEQA remains underexplored due to the lack of tailored benchmarks. |
| Approach: | They propose a scalable multi-document, multi-entity benchmark to evaluate LLMs' capacity to retrieve, consolidate, and reason over scattered and dense information. |
| Outcome: | The proposed benchmarks show that even advanced models achieve only 59% accuracy on MEBench. |
TelAgentBench: A Multi-faceted Benchmark for Evaluating LLM-based Agents in Telecommunications (2025.emnlp-industry)
Copied to clipboard
Sunwoo Lee, Daseong Jang, Dhammiko Arya, Gyoung-eun Han, Injee Song, SaeRom Kim, Sangjin Kim, Seojin Lee, Seokyoung Hong, Sereimony Sek, Seung-Mo Cho, Sohee Park, Sungbin Yoon, Wonbeom Jang, Eric Davis
| Challenge: | Large Language Models (LLMs) are becoming powerful agentic systems . generic benchmarks fail to assess realistic, non-English performance . |
| Approach: | They propose to evaluate five core agentic capabilities: Reasoning, Planning, Action (tool-use), Retrieval-Augmented Generation, and Instruction Following. |
| Outcome: | The evaluations reveal significant performance disparities between models that employ explicit reasoning and those that do not. |
Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models (2026.findings-acl)
Copied to clipboard
Mohammad Mahdi Abootorabi, Omid Ghahroodi, Anas Madkoor, Marzia Nouri, Doratossadat Dastgheib, Ehsaneddin Asgari
| Challenge: | Existing evaluations focus on piecemeal or disconnected tasks, obscuring critical cognitive weaknesses and providing little insight for targeted improvement. |
| Approach: | They propose a bilingual, cognitively human-grounded multimodal benchmark for VLMs that evaluates six levels of cognition through carefully designed image–question–answer tasks. |
| Outcome: | The proposed framework ensures scalability, cultural inclusivity, and linguistic fidelity. |
AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions. |
| Approach: | They propose a benchmark that provides more nuanced evaluations of alignment capabilities for large Vision-Language Models (VLMs) they use a rule-calibrated evaluator that exceeds GPT-4's evaluation ability and a “alignment score” to assess the robustness and stability of models across diverse prompts. |
| Outcome: | The proposed benchmark covers 13 tasks across three categories and includes both single-turn and multi-turn dialogue scenarios. |