KOR-Bench: Benchmarking Language Models on Knowledge-Orthogonal Reasoning Tasks (2410.06526v3)

Abstract: In this paper, we introduce Knowledge-Orthogonal Reasoning (KOR), a concept aimed at minimizing reliance on domain-specific knowledge, enabling more accurate evaluation of models' reasoning abilities in out-of-distribution settings. Based on this concept, we propose the Knowledge-Orthogonal Reasoning Benchmark (KOR-Bench), encompassing five task categories: Operation, Logic, Cipher, Puzzle, and Counterfactual. KOR-Bench emphasizes models' effectiveness in applying new rule descriptions to solve novel rule-driven questions. O1-Preview and O1-Mini achieve accuracies of 72.88% and 70.16%, surpassing Claude-3.5-Sonnet and GPT-4o (58.96% and 58.00%), highlighting the effectiveness of KOR-Bench. We perform detailed analyses, identifying bottlenecks in the Cipher task with Stepwise Prompting, where two rounds of Self-Correction yield optimal results. We evaluate performance across three integrated tasks, explore the impact of Tricks on the Puzzle task, and visualize rule-focused attention. Additionally, we conduct an ablation study on dataset size, benchmark correlations, and zero-shot and three-shot "only questions" experiments. KOR-Bench aims to enhance reasoning evaluation and support further research in this area.

• The paper presents KOR-Bench, a novel benchmark that isolates intrinsic reasoning from domain-specific knowledge.
• It details five task categories—Operation, Logic, Cipher, Puzzle, and Counterfactual—to challenge models in out-of-distribution scenarios.
• Analyses reveal significant performance gaps in top models and highlight key areas for improving self-correction and complexity handling.

Analysis of "KOR-Bench: Benchmarking LLMs on Knowledge-Orthogonal Reasoning Tasks"

Overview

The paper presents KOR-Bench, a specialized benchmark aimed at evaluating the reasoning abilities of LLMs in scenarios devoid of domain-specific knowledge influence. It introduces the novel concept of Knowledge-Orthogonal Reasoning (KOR) to ensure a more accurate assessment of models' intrinsic reasoning capabilities, particularly for Out-of-Distribution (OOD) tasks.

Key Contributions

1. Knowledge-Orthogonal Reasoning (KOR): This concept emphasizes the evaluation of reasoning skills independent of domain knowledge acquired during pre-training. KOR allows the disentanglement of reasoning abilities from memorized knowledge patterns.
2. Benchmark Composition: KOR-Bench comprises five diverse task categories—Operation, Logic, Cipher, Puzzle, and Counterfactual—designed to challenge models with new rule representations and environments:
   • Operation Reasoning Task: Evaluates mathematical reasoning with novel symbolic operators.
   • Logic Reasoning Task: Assesses the application of logical rules and understanding of logical concepts.
   • Cipher Reasoning Task: Tests decryption and encryption abilities using unexplored rules.
   • Puzzle Reasoning Task: Focuses on problem-solving in spatial and verbal puzzles.
   • Counterfactual Reasoning Task: Ensures adaptability to hypothetical scenarios.
3. Model Evaluation: The paper tests several state-of-the-art LLMs using zero-shot and three-shot evaluation strategies on KOR-Bench. Notably, the results show significant challenges for even top-performing models, such as Claude-3.5-Sonnet and GPT-4o, which achieved accuracies of 58.96% and 58.00%, respectively.

Detailed Analysis

• Bottleneck Identification: The paper explores specific task categories to identify bottlenecks, particularly in Cipher reasoning, where spatial operations were highlighted as major difficulties.
• Self-Correction Mechanism: Analysis of iterative self-correction revealed that substantial accuracy improvements were predominantly observed in the initial rounds of correction.
• Complex Task Processing: Tests conducted under complex reasoning conditions demonstrated varied resilience and adaptability across models, shedding light on the need for improved task comprehension mechanisms.

Implications

1. Theoretical Impact: KOR-Bench paves the way for the development of new evaluation frameworks that focus on intrinsic reasoning capabilities rather than memorization, highlighting the necessity for model architectures and training processes that can independently generalize reasoning skills.
2. Practical Impact: The benchmark could significantly aid in identifying true reasoning capabilities of LLMs, providing valuable insights for both developers and researchers in improving model robustness and reliability in real-world applications.
3. Future Directions: Future expansions are suggested to increase dataset size, introduce dynamic rule configurations, and refine evaluation metrics. Notably, extending KOR-Bench into a multimodal framework could open new research avenues in reasoning tasks across different domains, such as vision and language.

Conclusion

KOR-Bench represents a critical step towards a nuanced understanding of reasoning in AI systems. By isolating reasoning abilities from pre-existing knowledge, this benchmark challenges current models and sets the stage for the next generation of AI research focused on true cognitive capabilities.