CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities. |
| Approach: | They propose a Cognition-Domain-Task framework which measures a model’s capabilities across three dimensions. |
| Outcome: | The proposed framework improves performance on dataset evaluation and data selection, while achieving higher scores on general and specific benchmarks. |
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Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
SCAN: Structured Capability Assessment and Navigation for LLMs (2026.acl-long)
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| Challenge: | Existing research has focused on approximating model rankings, but such benchmarks fail to provide users and developers with a comprehensive and fine-grained understanding of a specific model’s capabilities. |
| Approach: | They propose a framework that enables detailed characterization of LLM capabilities through comprehensive and fine-grained evaluation. |
| Outcome: | The proposed framework enables detailed characterization of large language models through comprehensive and fine-grained evaluation. |
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step (2024.acl-long)
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Zehui Chen, Weihua Du, Wenwei Zhang, Kuikun Liu, Jiangning Liu, Miao Zheng, Jingming Zhuo, Songyang Zhang, Dahua Lin, Kai Chen, Feng Zhao
| Challenge: | Existing studies evaluate the tool utilization ability of large language models based on the final output or only consider the single-step tool calling. |
| Approach: | They propose a new approach to evaluate the tool utilization capability of large language models (LLMs) they decompose the tool usage into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. |
| Outcome: | The proposed model exhibits consistency with the outcome-oriented evaluation and provides a more fine-grained analysis of the capabilities of LLMs. |
GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)
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| Challenge: | Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results. |
| Approach: | They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
| Outcome: | The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
On Evaluating LLMs’ Capabilities as Functional Approximators: A Bayesian Evaluation Framework (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized the way we can formulate tasks in text-in-text-out format. |
| Approach: | They propose a new evaluation framework to comprehensively assess LLMs’ function modeling abilities by adopting a Bayesian perspective of function modeling. |
| Outcome: | The proposed evaluation framework enables LLMs to excel in utilizing prior knowledge to develop a strong understanding of the underlying function. |
FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are evaluated by overall performance on various text understanding and generation tasks. |
| Approach: | They propose a framework for Fine-grAined and Cognition-grounded LLMs’ Capability Evaluation that dissociates the language-related capabilities from cognition-related ones. |
| Outcome: | The proposed framework dissociates the language-related capabilities from cognition-related ones and breaks down the process of applying a specific capability into three sub-steps: recalling relevant knowledge, utilizing knowledge, and solving problems. |
GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated significant capabilities in processing and understanding text data. |
| Approach: | They propose a structure-based instruction-based method to enhance LLM performance on complex graph tasks. |
| Outcome: | The proposed framework outperforms open-source models on graph problem-solving, but the gap is narrowing. |
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation (2026.findings-acl)
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| Challenge: | Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models. |
| Approach: | They evaluate off-the-shelf and instruction-tuned graph learning models across a variety of scenarios. |
| Outcome: | The proposed models outperform traditional graph learning models in few-shot settings, the authors show . their models out perform models with instruction tuning, and they show excellent generalization and robustness. |
CogLM: Tracking Cognitive Development of Large Language Models (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, but few studies have explored the reasons behind the evolutionary relationship among various abilities. |
| Approach: | They construct a benchmark CogLM based on Piaget's Theory of Cognitive Development (PTC) which measures the cognitive levels of Large Language Models (LLMs) using 1,220 questions spanning 10 cognitive abilities crafted by more than 20 human experts. |
| Outcome: | The proposed framework provides a comprehensive testbed for the cognitive levels of LLMs. |
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench (2023.findings-emnlp)
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| Challenge: | a recent study shows that large language models can be used to predict performance on new configurations. |
| Approach: | They investigate the predictability of large language model capabilities by using BIG-bench . they find a subset of BIG-Bench tasks as informative as BIG-bnch Hard . |
| Outcome: | The proposed model achieves an R2 score greater than 95% on BIG-bench . the model is 3 smaller than BIG-Bench Hard, and the model performs better on the full set. |