Turtle Benchmark

Evaluating the logical reasoning and context comprehension abilities of large language models.

CommonProductProgrammingBenchmarkingLogical Reasoning

Turtle Benchmark is a new, cheat-proof benchmark based on the 'Turtle Soup' game, focusing on the assessment of large language models (LLMs) in terms of logical reasoning and context comprehension. By eliminating the need for background knowledge, it provides objective and unbiased test results with quantifiable outcomes, ensuring that models cannot be 'gamed' through the use of real user-generated questions.

Clear and unbiased objectives: Focuses on reasoning capabilities without requiring background knowledge.
Quantifiable results: Offers clear
measurable outcomes (correct/incorrect/unknown) for easy comparison.
Continual evolution: Utilizes real user-generated questions to prevent system manipulation.
Language understanding: Tests the model’s ability to comprehend context and perform logical inference.
Ease of use: Evaluations can be conducted through simple command line operations.
Rich data: Comprised of 32 unique 'Turtle Soup' stories and 1537 manually labeled annotations.
Results interpretation: Compare different models' overall accuracy and average story accuracy in 2-shot learning scenarios using scatter plots.

\Turtle Benchmark is designed for researchers and developers who need to evaluate and compare the performance of large language models. It is particularly suited for professionals focused on the model's logical reasoning and context comprehension capabilities
helping them gain a more accurate understanding of the model's performance in Chinese contexts.\

Researchers use Turtle Benchmark to assess the performance of different large language models on specific logical reasoning tasks.
Developers utilize Turtle Benchmark to test if their language models can accurately understand and respond to user queries.
Educational institutions use Turtle Benchmark as a teaching tool to help students understand how large language models operate and methods for performance evaluation.

1. Navigate to the Turtle Benchmark project directory.
2. Rename the .env.example file to .env and add your API key.
3. Execute the command `python evaluate.py` to conduct a 2-shot learning evaluation.
4. For zero-shot evaluation
run the command `python evaluate.py --shot 0`.

Visit

Turtle Benchmark Visit Over Time

Monthly Visits

488643166

Bounce Rate

37.28%

Page per Visit

5.7

Visit Duration

00:06:37

Turtle Benchmark Visit Trend

Turtle Benchmark Visit Geography

Turtle Benchmark Traffic Sources

Turtle Benchmark Alternatives

llm-colosseum — Evaluate large language models through Street Fighter 3 battles.

Programming•Artificial Intelligence•Benchmarking

browser-use — An open-source web automation library that supports any large language model (LLM)

Programming•Automation•Web Interaction

PARTNR — Benchmarking for Multi-Agent Task Planning and Reasoning

Others•Multi-Agent•Natural Language Processing

CoI-Agent — An intelligent agent for innovative research utilizing large language models (LLM)

Productivity•Research•Innovation

Prompt Engineering — A comprehensive resource library for Prompt Engineering techniques.

Productivity•Prompt Engineering•Machine Learning

LLMWare — AI Tools Built for Complex Enterprises

Productivity•Enterprise•Finance

252

Cheating LLM Benchmarks — A research project that explores cheating behaviors in automated language model benchmarking.

Programming•Natural Language Processing•Machine Learning

Platea AI — Quick access to high-quality prompts

Productivity•Language Models•Comparison Tool

Entropy-based Sampling — Entropy-based sampling technique that optimizes the diversity and accuracy of model outputs

Programming•Machine Learning•Natural Language Processing

Show-Me — A transparent and visual open-source alternative to ChatGPT.

Programming•Open Source•Visualization

Stability AI — Unlocking human potential through generative AI.

396

g1 — Using the open-source model Llama-3.1 70b to create a reasoning chain similar to o1 on Groq.

Programming•Artificial Intelligence•Logical Reasoning

rStar — Enhances problem-solving capabilities of small language models through self-play mutual reasoning.

Programming•Machine Learning•Natural Language Processing

Qwen2-Math — A next-generation mathematical model focused on solving complex mathematical problems.

Productivity•Mathematical Models•Language Models

498

redcache-ai — A dynamic memory framework that supports large language models and agents.

Programming•memory framework•language models

LAMDA-TALENT — Comprehensive Tabular Data Learning Toolbox and Benchmarking Platform

Programming•Tabular Data•Deep Learning

MoA — Mixture of Agents technology for enhancing large language model performance

Programming•Language Models•Performance Enhancement

186

HippoRAG — A novel RAG framework inspired by human long-term memory

Programming•Natural Language Processing•Knowledge Integration

198

LLM Comparator — Compares the output of various large language models (LLMs)

Productivity•Artificial Intelligence•Language Models

348

VQAScore — VQAScore, a novel evaluation metric and benchmark for text-to-vision generation, is introduced. VQAScore, based on the CLIP-FlanT5 model, achieves state-of-the-art performance in evaluating text-to-image/video/3D generation. It serves as a powerful alternative to CLIPScore. GenAI-Bench, a benchmark dataset, provides real-world testing texts with rich semantic combinations, allowing for a comprehensive assessment of generative model performance.

Image•Text generation•Vision generation

150

EasyContext — EasyContext demonstrates how to leverage existing technologies to train language models with 700K and 1M context lengths.

Programming•Language Models•Context Length

126

GenAI-Arena — Benchmarking visual generation models

Image•Benchmarking•Visual Generation Models

108

ragobble — Transforms audio into LLM data.

Productivity•Audio to Text•Artificial Intelligence

234

LaVi-Bridge — Connects different language models and generative visual models for text-to-image generation

Image•Text-to-Image Generation•Language Models

600

Apollo LLM — Apollo is a platform for a multilingual medical domain, offering models, datasets, benchmarks, and code repositories.

Productivity•Artificial Intelligence•Medical AI

582

Best AI Websites & Tools