Can Language Models Play the Wikipedia Game?
cs.AI, cs.IR
Submitted: 2026-10-09
Updated: 2026-10-09
Code: https://github.com/alan-turing-institute/ARC-Wiki-Game
Sources
- The Llama 3 Herd of Models
- Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings
- WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
- Understanding the planning of LLM agents: A survey
- An evaluation of LLM code generation capabilities through graded exercises
- Language-agnostic Topic Classification for Wikipedia
- A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond
- Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- Article Classification with Graph Neural Networks and Multigraphs
- Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data
- Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs
- Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
- Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena
- Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
- Gemma 2: Improving Open Language Models at a Practical Size
- WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset
- MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
- On Memorization of Large Language Models in Logical Reasoning
- LinkBERT: Pretraining Language Models with Document Links
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems