LLM Evaluation

LLM: Large Language Model

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194 papers in the last four weeks, up 87% on the four weeks before. 1.9% of all new papers.

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Do Emotions Influence Moral Judgment in Large Language Models?
Apr 21, 2026Mohammad Saim, Tianyu JiangLLM Evaluation Moral Reasoning in Language Models
University of Cincinnati
HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing
Apr 21, 2026Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo +7LLM Evaluation LLM-as-a-Judge
Department of Computer Science and Technology, Tsinghua University
DW-Bench: Benchmarking LLMs on Data Warehouse Graph Topology Reasoning
Apr 21, 2026Ahmed G. A. H Ahmed, C. Okan SakarLLM Evaluation LLM Reasoning with Graphs
Innosol / Bahçe¸sehir University · Department of Computer Engineering, Bahcesehir University, Turkey
Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest
Apr 21, 2026Ramtin Davoudi, Kartik Thakkar, Nazanin Donyapour +2Social Media Analysis LLM Evaluation
Utah State University · Independent Researcher · Vanderbilt University
Personalized Benchmarking: Evaluating LLMs by Individual Preferences
Apr 21, 2026Cristina Garbacea, Heran Wang, Chenhao TanLLM Evaluation User Preference Modeling
University of Chicago
LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification
Apr 20, 2026Pedro Barbosa de Carvalho NetoLegal NLP LLM Evaluation
Teresina, PI, Brazil
Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models
Apr 20, 2026Seyedali Mohammadi, Manas Gaur, Francis FerraroLLM Evaluation Scientific Reasoning in Language Models
University of Maryland, Baltimore County, MD, USA
Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion
Apr 20, 2026Terry LeitchLLM Quantization Dynamical Systems
Ruxton AI
FUSE: Ensembling Verifiers with Zero Labeled Data
Apr 20, 2026Joonhyuk Lee, Virginia Ma, Sarah Zhao +4Neural Network Verification LLM Evaluation
Stanford University · Google
Correction and Corruption: A Two-Rate View of Error Flow in LLM Protocols
Apr 20, 2026Fernando ReitichLLM Evaluation LLM Auditing
Imens, LLC · Weston, Florida, USA
STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs
Apr 20, 2026Sungeun An, Swanand Ravindra Kadhe, Shailja Thakur +2LLM Evaluation Reasoning Evaluation
IBM Research
Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation
Apr 20, 2026Ran Zhang, Steffen Eger, Arda Tezcan +3Creativity Assessment LLM Evaluation
University of Mannheim, Data and Web Science Group · University of Technology Nuremberg (UTN), Department Engineering · 5Natural Language Learning and Generation (NLLG) Lab +2
Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks
Apr 20, 2026Jin Zhao, Marta Knežević, Tanja KäserLLM Evaluation Intelligent Tutoring Systems
†Work done during exchange at EPFL.
BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models
Apr 20, 2026Naveen Bera, Pulijala Sai Nikhila, Kondaguduru Abhiram +6LLM Evaluation Hallucination Detection
LLM Lens
How Creative Are Large Language Models in Generating Molecules?
Apr 20, 2026Wen Tao, Yiwei Wang, Peng Zhou +6Creativity Assessment LLM Evaluation
Nanyang Technological University · University of California, Merced · Hunan University +1
Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design
Apr 20, 2026Xinyao Zhang, Nicole Sonne Heckmann, Manuela Del Castillo Suero +2LLM Evaluation Healthcare
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
ltzGLUE: Luxembourgish General Language Understanding Evaluation
Apr 20, 2026Alistair Plum, Felicia Körner, Anne-Marie Lutgen +8LLM Evaluation Text Classification
University of Luxembourg, Luxembourg · LMU Munich, Germany · Munich Center for Machine Learning, Germany +2
From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?
Apr 20, 2026Hasan Amin, Harry Yizhou Tian, Xiaoni Duan +3LLM Evaluation LLM-Assisted Annotation
Purdue University · Washington University in St. Louis
QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks
Apr 20, 2026Taylor Lundy, Narun K. Raman, Kevin Leyton-BrownLLM Evaluation Bayesian Optimization
Department of Computer Science University of British Columbia Vancouver, BC
SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks
Apr 20, 2026Mohammadtaher Safarzadeh, Hitesh Laxmichand Patel, Afshin Orojlooyjadid +2LLM Evaluation Benchmark Contamination
Oracle AI
Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks
Apr 20, 2026Rongyuan Tan, Jue Zhang, Zhuozhao Li +3LLM Evaluation Feature Attribution
1Southern University of Science and Technology, China · *Work done during an internship at Microsoft. · 2Microsoft
MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models
Apr 20, 2026Suhyun Lee, Palakorn Achananuparp, Neemesh Yadav +2Mental Health Counseling LLM Evaluation
Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea · School of Computing and Information Systems, Singapore Management University, Singapore
PoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference
Apr 20, 2026Arther Tian, Alex Ding, Frank Chen +2LLM Evaluation LLM-as-a-Judge
DGrid AI
Concurrent Criterion Validation of a Validity Screen for LLM Confidence Signals via Selective Prediction
Apr 20, 2026Jon-Paul CacioliLLM Evaluation Selective Prediction
Independent Researcher, Melbourne, Australia
Screen Before You Interpret: A Portable Validity Protocol for Benchmark-Based LLM Confidence Signals
Apr 20, 2026Jon-Paul CacioliLLM Evaluation Psychometric Validation
Independent Researcher, Melbourne, Australia
Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance
Apr 19, 2026Parker Seegmiller, Sarah Masud PreumLLM Evaluation Instruction Following
Department of Computer Science Dartmouth College Hanover, NH, USA
KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models
Apr 19, 2026Xiao Zhang, Qianru Meng, Yongjian Chen +2LLM Evaluation Compositional Reasoning
University of Groningen · LIACS, Leiden University
Beyond Static Snapshots: A Grounded Evaluation Framework for Language Models at the Agentic Frontier
Apr 19, 2026Jazmia HenryReward Hacking RL for Language Models
University of Oxford
Evaluating Hallucinations in Domain-Adapted Large Language Models
Apr 19, 2026Sanchita Porwal, Sai Prasath S, Xingjian Bi +1LLM Evaluation Domain Adaptation
College of Computing Georgia Institute of Technology Atlanta, GA 30318, USA
ArgBench: Benchmarking LLMs on Computational Argumentation Tasks
Apr 19, 2026Yamen Ajjour, Carlotta Quensel, Nedim Lipka +1LLM Evaluation Argument Mining
Leibniz University Hannover · Adobe Research · Leibniz University Hannover, L3S

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Do Emotions Influence Moral Judgment in Large Language Models?

Mohammad Saim, Tianyu Jiang

University of Cincinnati

LLM Evaluation Moral Reasoning in Language Models Moral Reasoning

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Large language models have been extensively studied for emotion recognition and moral reasoning as distinct capabilities, yet the extent to which emotions influence moral judgment remains underexplored. In this work, we develop an emotion-induction pipeline that infuses emotion into moral situations and evaluate shifts in moral acceptability across multiple datasets and LLMs. We observe a directional pattern: positive emotions increase moral acceptability and negative emotions decrease it, with effects strong enough to reverse binary moral judgments in up to 20% of cases, and with susceptibility scaling inversely with model capability. Our analysis further reveals that specific emotions can sometimes behave contrary to what their valence would predict (e.g., remorse paradoxically increases acceptability). A complementary human annotation study shows humans do not exhibit these systematic shifts, indicating an alignment gap in current LLMs.

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