LLM Prompting

LLM: Large Language Model

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22 papers in the last four weeks, up 22% on the four weeks before. 0.2% of all new papers.

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Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning
Jun 13, 2026Ali Dasdan, Manan Shah, W. Russell Neuman +3LLM Alignment Moral Reasoning in Language Models
1KD Consulting, CA, USA · 2New York University, NY, USA
LLMs Can Better Capture Human Judgments--With the Right Prompts
Jun 10, 2026Danica Dillion, Chen Cecilia Liu, Baihui Wang +5Human Preference Evaluation LLM Alignment
Complexity Science Hub, Vienna · The Ohio State University · University of Cambridge +2
Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation
Jun 10, 2026Zahra Habibzadeh, Paria Khoshtab, Amir Mesbah +1LLM Evaluation LLM Grounding
Tehran Institute for Advanced Studies, Khatam University, Iran
TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
Jun 10, 2026Zhiyi Chen, Jie Song, Peng LiLLM Prompting Text-to-SQL
ByteDance Inc. · Georgia Institute of Technology · Work done during an internship at the ByteBrain team, ByteDance
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
Jun 9, 2026Fei Wang, Si Si, Cho-Jui Hsieh +1Evolutionary Optimization LLM Prompting
Google · UCLA
A Constrained Natural-Language Interface for Variational Multi-Physics Finite Element Simulations in FEniCS
Jun 9, 2026Nilay Upadhyay, Wesley F. ReinhartPDE Solving LLM Prompting
Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802, USA · Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA
How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?
Jun 8, 2026Kateryna Karpo, Artem ChernodubLLM Evaluation LLM Prompting
Ukrainian Catholic University · YouScan · Zendesk
Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses
Jun 7, 2026Qilin Zhou, Zhuo Wang, Yue Li +1LLM Evaluation LLM-as-a-Judge
City University of Hong Kong, Hong Kong SAR, China
How reliable are LLMs when it comes to playing dice?
Jun 5, 2026Luca Avena, Gianmarco Bet, Bernardo BusoniLLM Evaluation LLM Prompting
Università degli Studi di Firenze Dipartimento di Matematica e Informatica ‘Ulisse Dini’
Scaffold, Not Vocabulary? A Controlled, Two-Tier, Pre-Registered Study of a Popperian Code-Generation Skill
Jun 4, 2026Mehmet IscanLLM Prompting Code Generation Evaluation
PythaLab, Yıldız Technical University, Istanbul, Turkey
Contextualized Prompting For Stance Detection On Social Media
Jun 4, 2026Tilman Beck, Shakib Yazdani, Simon Kruschinski +2Social Media Analysis Stance Detection
Institute of Intensive Care, University Hospital of Zurich and University of Zurich · Institute of Computer Science, University of Goettingen · GESIS Leibniz Institute for the Social Sciences +2
The Self-Correction Illusion: LLMs Correct Others but Not Themselves
Jun 4, 2026Kuan-Yen Chen, Fang-Yi Su, Jung-Hsien ChiangLLM Self-Correction LLM Prompting
National Cheng Kung University
Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting
Jun 4, 2026Michiro Asai, Ailiang Lin, Yu Kishimoto +4LLM Prompting Factual Knowledge in Language Models
Institute of Science Tokyo
Interpreting Style Representations via Style-Eliciting Prompts
Jun 4, 2026Junghwan Kim, David JurgensLLM Prompting LLM Interpretability
University of Michigan
CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
Jun 3, 2026Shanu Kumar, Shubhanshu Khandelwal, Akhila Yesantarao Venkata +3LLM Prompting Cost-Aware Inference
MBZUAI · Microsoft
SANE Schema-aware Natural-language Evaluation of Biological Data
Jun 3, 2026Rolf Gattung, Martin Krueger, Markus ReischlLLM Evaluation LLM Grounding
Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
Jun 2, 2026Zhengyi Zhao, Shubo Zhang, Huimin Wang +7LLM Prompting Instruction Following
The Chinese University of Hong Kong · Tencent Jarvis Lab · Westlake University +2
Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing
Jun 2, 2026Mehmet Utku ColakAI Coding Agents LLM Prompting
Department of Computer Engineering, Istanbul Technical University · Istanbul, Turkey
The Invisible Lottery: How Subtle Cues Steer Algorithm Choice in LLM Code Generation
Jun 2, 2026Akanksha Narula, Mofasshara Binte Rafique, Laurent BindschaedlerPrompt Sensitivity LLM Prompting
Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, Germany · Independent Researcher, Switzerland.
Large Language Models Are Overconfident in Their Own Responses
Jun 2, 2026Mario Sanz-Guerrero, Manuel Mager, Katharina von der WenseConfidence Estimation in Language Models LLM Prompting
∇Johannes Gutenberg University Mainz, Germany · ♠University of Colorado Boulder, USA
The Unsampled Truth: Quantifying Prompt Artifacts in LM Psychometrics
Jun 2, 2026Nils Schwager, Christoph Hau, Simon Münker +1Prompt Sensitivity LLM Prompting
Trier University, Trier, Germany
From Script to Semantics: Prompting Strategies for African NLI
Jun 2, 2026Anuj Tiwari, Terry Oko-odion, Hannah NwokochaMultilingual Language Model Evaluation LLM Prompting
Noida Institute of Engineering and Technology1, India1 · ML Collective1,2,3, Nigeria2,3
Decomposing how prompting steers behavior
Jun 2, 2026Fan L. Cheng, Nikolaus KriegeskorteLLM Prompting LLM Interpretability
Columbia University
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Jun 1, 2026Amir Ghasemian, Homa Hosseinmardi, Upasana Dutta +1LLM Prompting LLM Reranking
Department of Communication, University of California, Los Angeles, CA 90095 · Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104 · Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104 +1
Visual Graph Scaffolds for Structural Reasoning in Large Language Models
Jun 1, 2026Runlin Lei, Xiaokui Xiao, Zhewei WeiLLM Reasoning with Graphs LLM Prompting
Renmin University of China · National University of Singapore
Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs
May 30, 2026Yu-An Lu, Ci-Yang Tsai, Yu-Lin Tsai +2LLM Prompting Privacy Leakage in Language Models
National Yang Ming Chiao Tung University · UC Berkeley
Anchorless Diversification for Parallel LLM Ideation
May 28, 2026Fares Nabil Ibrahim, Nafis Saami Azad, Raiyan Abdul BatenLLM Inference Efficiency LLM Inference
Bellini College of Artificial Intelligence, Cybersecurity, and Computing, University of South Florida, USA
Spurious Prompts: Can Irrelevant Prompts Steer Large Language Models?
May 28, 2026Pawel Batorski, Abtin Pourhadi, Jerzy Sarosiek +2Prompt Sensitivity LLM Prompting
1Heinrich Heine University Düsseldorf · 2Jagiellonian University · 3IDEAS Research Institute
When Does Persona Prompting Actually Help? A Retrieval and Metric Analysis of Expert Role Injection in LLMs
May 28, 2026Shuai Xiao, Su Liu, Weikai Zhou +4LLM Evaluation LLM Prompting
Independent Researchers
UA-Legal-Bench: A Benchmark for Evaluating Large Language Models on Ukrainian Legal Reasoning
May 27, 2026Volodymyr OvcharovMultilingual Language Model Evaluation Legal NLP
SecondLayer

Jun 13, 2026, cs.AIJ/K move · Enter open · S save

Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning

Ali Dasdan, Manan Shah, W. Russell Neuman, Chad Coleman+2

1KD Consulting, CA, USA · 2New York University, NY, USA

LLM Alignment Moral Reasoning in Language Models LLM Auditing LLM Prompting LLM Interpretability Framing Effects in Language Models

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Behavioral audits of Large Language Models on moral prompts measure what the model says, not the internal computation producing it. We use Transluce, an AI-driven mechanistic-interpretability platform, to examine LLaMA 3.1-8B-Instruct on 54 moral prompts in four batteries: 17 dilemmas, policy, and meta-ethical questions (B1); 6 role-playing scenarios (B3); and a controlled trolley contrast varying the switching mechanism with people fixed (B4, 15 prompts) or identity attributes with mechanism fixed (B5, 16 prompts). Two complementary metric families, five cluster-level metrics and a six-metric neuron-level panel, converge on a Situational Anchor Effect: domain-specific representations dominate the top of the activation list across every battery. The model's ethics-labeled capacity stays essentially constant; its salience (rank, priority, top-of-list presence) is highly sensitive to the interpretive frame the prompt selects. The B4-vs-B5 contrast confirms the model attends to whichever surface feature varies: aggregate ethics metrics are indistinguishable, but the dominant non-ethics distractor mirrors the design. A multi-temperature audit identifies a candidate ethics neuron (L16/N3837) stable across temperatures; a cross-model behavioral proxy on two frontier models yields preliminary evidence of divergence in self-reported moral focus, consistent with an Alignment Wrapper in which RLHF re-orders surface text without removing underlying domain-first frames. We unify these as Frame-Conditioned Moral Computation: the prompt's surface vocabulary selects a feature manifold, and the moral conclusion is downstream of that selection. Behavioral alignment must be supplemented by Mechanistic Alignment: a research program asking whether ethics-related features can be shown causally privileged under controlled frame variation, not merely loud in the explanation.

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