Yixin Liu, Kejian Shi, Alexander Fabbri, Yilun Zhao, PeiFeng Wang, Chien-Sheng Wu, Shafiq Joty, Arman Cohan
| Challenge: | Existing evaluations of large language models (LLMs) for instruction following are incomplete. |
| Approach: | They propose to use 25 base LLMs and 15 recently proposed evaluation protocols to evaluate instruction following on 4 human-annotated datasets. |
| Outcome: | The proposed evaluations identify the best-performing base LLMs and evaluation protocols with a high degree of robustness. |
Similar Papers
MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation methods focus on single-language scenarios, overlooking multilingual and cross-lingual contexts. |
| Approach: | They propose a tool to assess instruction-following capabilities across 23 different languages with 1667 verifiable instruction tasks. |
| Outcome: | MaXIFE evaluates instruction-following capabilities across 23 languages with 1667 verifiable instruction tasks. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
Copied to clipboard
Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |
Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself. |
| Approach: | This tutorial will lay the foundations and explain the basics of evaluation and compare traditional methods to newly developed methods. |
| Outcome: | The tutorial assumes little familiarity with metrics, datasets, prompts and benchmarks . it will compare traditional methods to newly developed methods . |
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation (2025.acl-industry)
Copied to clipboard
| Challenge: | CodeIF assesses the ability of large language models to adhere to task-oriented instructions in code generation tasks. |
| Approach: | They introduce a benchmark designed to assess LLMs' ability to adhere to task-oriented instructions within diverse code generation scenarios. |
| Outcome: | The proposed benchmark assesses LLMs' ability to adhere to task-oriented instructions in code generation tasks across a wide range of complexity levels and programming domains. |
The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models (2024.findings-emnlp)
Copied to clipboard
Xinyi Chen, Baohao Liao, Jirui Qi, Panagiotis Eustratiadis, Christof Monz, Arianna Bisazza, Maarten Rijke
| Challenge: | Current evaluation resources for instruction following focus on single task instructions, but the instruction sequences in these benchmarks often lack coherence. |
| Approach: | They propose to evaluate models’ abilities to follow multiple instructions through sequential instruction following tasks using four tasks to assess different aspects of sequential instruction followed. |
| Outcome: | The proposed benchmark outperforms open-source and closed-source models on four tasks assessing different aspects of sequential instruction following. |
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs. |
| Approach: | They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts. |
| Outcome: | The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts. |
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization (2024.findings-naacl)
Copied to clipboard
Yixin Liu, Alexander Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, Arman Cohan
| Challenge: | Recent studies have found that large language models (LLMs) can achieve state-of-the-art performance on generic summarization benchmarks, but their performance on more complex summarizing task settings is less studied. |
| Approach: | They benchmark large language models on instruction controllable text summarization . they use 4 evaluation protocols and 11 LLMs to evaluate their performance . |
| Outcome: | The proposed model performs well on instruction controllable text summarization tasks with 4 evaluation protocols and 11 LLMs. |
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
Copied to clipboard
Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
| Challenge: | Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains. |
| Approach: | They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks . |
| Outcome: | The proposed evaluations are reproducible, reliable, and robust. |
Assessing the Capabilities of Large Language Models in Coreference: An Evaluation (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are a new approach to coreference resolution, but their performance is not yet fully understood. |
| Approach: | They propose that future efforts should improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
| Outcome: | The proposed methods improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
Copied to clipboard
| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
| Approach: | They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them. |
| Outcome: | The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods. |