General365: Benchmarking General Reasoning in Large Language Models Across Diverse and Challenging Tasks

General365 is a new benchmark for rigorously evaluating Large Language Models' (LLMs) general reasoning abilities across diverse and challenging tasks. It helps AI practitioners move beyond specialized benchmarks to assess true general intelligence and build more reliable AI applications.

intermediate15 min5 steps

The play

Recognize Specialized Benchmark Limitations
Understand that relying solely on LLM performance in specialized domains (e.g., math, physics) can lead to an overestimation of true general intelligence. Specialized benchmarks don't fully capture an LLM's ability to generalize.
Understand General365's Purpose
Familiarize yourself with benchmarks like General365, which are designed to assess broader, more general reasoning capabilities by incorporating a diverse array of challenging tasks beyond narrow expertise.
Integrate Diverse Evaluation Strategies
Incorporate general reasoning benchmarks into your LLM evaluation and selection processes. This ensures a comprehensive assessment of a model's ability to generalize across various contexts, not just specific domains.
Prioritize Generalization in Model Selection
When selecting or developing LLMs, prioritize models that demonstrate robust generalization across diverse tasks as measured by comprehensive benchmarks. This leads to more reliable AI applications in varied, real-world scenarios.
Support Research for General Reasoning
Contribute to or advocate for ongoing research and development into architectural and training improvements that foster genuine general reasoning in LLMs, moving beyond domain-specific expertise.

Starter code

def evaluate_llm(model_name: str, benchmarks: list[dict]) -> dict:
    """Simulates evaluating an LLM against a list of benchmarks.
    This highlights the need for diverse evaluation, including general reasoning.
    """
    results = {}
    print(f"\nEvaluating {model_name}...")
    for benchmark in benchmarks:
        print(f"  Running {benchmark['name']} ({benchmark['type']})...")
        # Placeholder for actual evaluation logic
        # In a real scenario, this would call specific benchmark execution code.
        score = (len(model_name) * 7 + len(benchmark['name']) * 3) % 100
        if benchmark['type'] == 'general-reasoning':
            score = min(score + 15, 95) # Simulate higher importance/difficulty
        results[benchmark['name']] = {"score": score, "type": benchmark['type']}
    return results

# Define a set of hypothetical benchmarks, including a general reasoning one
my_evaluation_suite = [
    {"name": "MathSolve", "type": "specialized"},
    {"name": "PhysicsQ&A", "type": "specialized"},
    {"name": "General365Reasoning", "type": "general-reasoning"},
    {"name": "CreativeTextGen", "type": "diverse-task"}
]

# Evaluate a hypothetical LLM_A
llm_a_performance = evaluate_llm("LLM_A_Specialist", my_evaluation_suite)
print("LLM_A Performance:", llm_a_performance)

# Evaluate a hypothetical LLM_B with better generalization
llm_b_performance = evaluate_llm("LLM_B_Generalist", my_evaluation_suite)
print("LLM_B Performance:", llm_b_performance)

# Example of how you might compare or prioritize
print("\n--- Comparison ---")
print(f"LLM_A General Reasoning Score: {llm_a_performance['General365Reasoning']['score']}")
print(f"LLM_B General Reasoning Score: {llm_b_performance['General365Reasoning']['score']}")
if llm_b_performance['General365Reasoning']['score'] > llm_a_performance['General365Reasoning']['score']:
    print("LLM_B shows stronger general reasoning, making it potentially more robust.")
else:
    print("LLM_A might be specialized, consider its overall generalization.")

Source

Paperarxiv.org