RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Yan, Jianhao, Luo, Yun, Zhang, Yue |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
RefuteBench 2.0 -- Agentic Benchmark for Dynamic Evaluation of LLM Responses to Refutation Instruction
by: Yan, Jianhao, et al.
Published: (2025)
by: Yan, Jianhao, et al.
Published: (2025)
T-REX: Table -- Refute or Entail eXplainer
by: Horstmann, Tim Luka, et al.
Published: (2025)
by: Horstmann, Tim Luka, et al.
Published: (2025)
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation
by: Sun, Zhouhao, et al.
Published: (2024)
by: Sun, Zhouhao, et al.
Published: (2024)
InFoBench: Evaluating Instruction Following Ability in Large Language Models
by: Qin, Yiwei, et al.
Published: (2024)
by: Qin, Yiwei, et al.
Published: (2024)
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
by: LI, Yizhi, et al.
Published: (2024)
by: LI, Yizhi, et al.
Published: (2024)
The Refutability Gap: Challenges in Validating Reasoning by Large Language Models
by: Mossel, Elchanan
Published: (2025)
by: Mossel, Elchanan
Published: (2025)
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
by: Sun, Wangtao, et al.
Published: (2024)
by: Sun, Wangtao, et al.
Published: (2024)
LIFEBench: Evaluating Length Instruction Following in Large Language Models
by: Zhang, Wei, et al.
Published: (2025)
by: Zhang, Wei, et al.
Published: (2025)
LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models
by: Ren, Huimin, et al.
Published: (2025)
by: Ren, Huimin, et al.
Published: (2025)
Beyond Correlation: Refutation-Validated Aspect-Based Sentiment Analysis for Explainable Energy Market Returns
by: van der Heever, Wihan, et al.
Published: (2026)
by: van der Heever, Wihan, et al.
Published: (2026)
KITE: A Benchmark for Evaluating Korean Instruction-Following Abilities in Large Language Models
by: Kim, Dongjun, et al.
Published: (2025)
by: Kim, Dongjun, et al.
Published: (2025)
Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels
by: Yan, Jianhao, et al.
Published: (2024)
by: Yan, Jianhao, et al.
Published: (2024)
Revisiting the Reliability of Language Models in Instruction-Following
by: Dong, Jianshuo, et al.
Published: (2025)
by: Dong, Jianshuo, et al.
Published: (2025)
LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
by: Zhang, Wei, et al.
Published: (2026)
by: Zhang, Wei, et al.
Published: (2026)
MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models
by: Ye, Junjie, et al.
Published: (2025)
by: Ye, Junjie, et al.
Published: (2025)
HREF: Human Response-Guided Evaluation of Instruction Following in Language Models
by: Lyu, Xinxi, et al.
Published: (2024)
by: Lyu, Xinxi, et al.
Published: (2024)
AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios
by: Qi, Yunjia, et al.
Published: (2025)
by: Qi, Yunjia, et al.
Published: (2025)
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
by: Li, Wei, et al.
Published: (2024)
by: Li, Wei, et al.
Published: (2024)
Evaluating Large Language Models in Analysing Classroom Dialogue
by: Long, Yun, et al.
Published: (2024)
by: Long, Yun, et al.
Published: (2024)
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
by: Wang, Peiding, et al.
Published: (2025)
by: Wang, Peiding, et al.
Published: (2025)
MCJudgeBench: A Benchmark for Constraint-Level Judge Evaluation in Multi-Constraint Instruction Following
by: Lee, Jaeyun, et al.
Published: (2026)
by: Lee, Jaeyun, et al.
Published: (2026)
Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models
by: Fu, Tingchen, et al.
Published: (2025)
by: Fu, Tingchen, et al.
Published: (2025)
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering
by: Si, Shuzheng, et al.
Published: (2025)
by: Si, Shuzheng, et al.
Published: (2025)
EmoBench: Evaluating the Emotional Intelligence of Large Language Models
by: Sabour, Sahand, et al.
Published: (2024)
by: Sabour, Sahand, et al.
Published: (2024)
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
by: Zhang, Yazhou, et al.
Published: (2024)
by: Zhang, Yazhou, et al.
Published: (2024)
Potential and Challenges of Model Editing for Social Debiasing
by: Yan, Jianhao, et al.
Published: (2024)
by: Yan, Jianhao, et al.
Published: (2024)
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
by: Qi, Yunjia, et al.
Published: (2024)
by: Qi, Yunjia, et al.
Published: (2024)
A Refutation of Shapley Values for Explainability
by: Huang, Xuanxiang, et al.
Published: (2023)
by: Huang, Xuanxiang, et al.
Published: (2023)
MedCalc-Bench: Evaluating Large Language Models for Medical Calculations
by: Khandekar, Nikhil, et al.
Published: (2024)
by: Khandekar, Nikhil, et al.
Published: (2024)
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
by: Parmar, Mihir, et al.
Published: (2024)
by: Parmar, Mihir, et al.
Published: (2024)
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
by: Feng, Jie, et al.
Published: (2024)
by: Feng, Jie, et al.
Published: (2024)
SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models
by: Lai, Peichao, et al.
Published: (2025)
by: Lai, Peichao, et al.
Published: (2025)
Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
by: Sakai, Yusuke, et al.
Published: (2025)
by: Sakai, Yusuke, et al.
Published: (2025)
Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging
by: Lyu, Mengxian, et al.
Published: (2026)
by: Lyu, Mengxian, et al.
Published: (2026)
Instruction Following by Principled Boosting Attention of Large Language Models
by: Guardieiro, Vitoria, et al.
Published: (2025)
by: Guardieiro, Vitoria, et al.
Published: (2025)
ChiEngMixBench: Evaluating Large Language Models on Spontaneous and Natural Chinese-English Code-Mixed Generation
by: Yang, Qingyan, et al.
Published: (2026)
by: Yang, Qingyan, et al.
Published: (2026)
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
by: Tu, Shangqing, et al.
Published: (2023)
by: Tu, Shangqing, et al.
Published: (2023)
TurkBench: A Benchmark for Evaluating Turkish Large Language Models
by: Toraman, Çağrı, et al.
Published: (2026)
by: Toraman, Çağrı, et al.
Published: (2026)
PsychCounsel-Bench: Evaluating the Psychology Intelligence of Large Language Models
by: Zeng, Min
Published: (2025)
by: Zeng, Min
Published: (2025)
Similar Items
-
RefuteBench 2.0 -- Agentic Benchmark for Dynamic Evaluation of LLM Responses to Refutation Instruction
by: Yan, Jianhao, et al.
Published: (2025) -
T-REX: Table -- Refute or Entail eXplainer
by: Horstmann, Tim Luka, et al.
Published: (2025) -
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation
by: Sun, Zhouhao, et al.
Published: (2024) -
InFoBench: Evaluating Instruction Following Ability in Large Language Models
by: Qin, Yiwei, et al.
Published: (2024) -
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
by: LI, Yizhi, et al.
Published: (2024)