NeedleBench: Can LLMs Do Retrieval and Reasoning in Information-Dense Context? (2407.11963v2)

Abstract: The capability of LLMs to handle long-context information is crucial across various real-world applications. Existing evaluation methods often rely either on real-world long texts, making it difficult to exclude the influence of models' inherent knowledge, or introduce irrelevant filler content to artificially achieve target lengths, reducing assessment effectiveness. To address these limitations, we introduce NeedleBench, a synthetic framework for assessing retrieval and reasoning performance in bilingual long-context tasks with adaptive context lengths. NeedleBench systematically embeds key data points at varying depths to rigorously test model capabilities. Tasks are categorized into two scenarios: information-sparse, featuring minimal relevant details within extensive irrelevant text to simulate simple retrieval tasks; and information-dense (the Ancestral Trace Challenge), where relevant information is continuously distributed throughout the context to simulate complex reasoning tasks. Our experiments reveal that although recent reasoning models like Deepseek-R1 and OpenAI's o3 excel in mathematical reasoning, they struggle with continuous retrieval and reasoning in information-dense scenarios, even at shorter context lengths. We also characterize a phenomenon termed 'under-thinking', where models prematurely conclude reasoning despite available information. NeedleBench thus provides critical insights and targeted tools essential for evaluating and improving LLMs' long-context capabilities. All resources are available at OpenCompass: https://github.com/open-compass/opencompass.

• The paper proposes NeedleBench as a robust framework to evaluate LLMs’ long-context retrieval and reasoning, incorporating tasks like the Ancestral Trace Challenge.
• The paper demonstrates that model performance varies significantly with context length and prompt sensitivity, revealing limitations in multi-retrieval and multi-reasoning tasks.
• The paper highlights practical and theoretical implications, urging the development of improved training methods for more effective, long-context logical reasoning.

Assessing the Long-Context Capabilities of LLMs with NeedleBench

The paper "NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million Context Window?" by Mo Li et al. presents a comprehensive framework for evaluating the long-context capabilities of LLMs. This paper underscores a crucial aspect in natural language processing: the effective retrieval and reasoning over extensive text lengths, a fundamental capability for applications like legal document retrieval, academic research, and business intelligence.

Overview of NeedleBench

NeedleBench is meticulously designed to assess bilingual long-context capabilities of LLMs, involving tasks across multiple length intervals (4k, 8k, 32k, 128k, 200k, 1000k and beyond). The methodology includes inserting critical data points at various depths within extensive texts to evaluate both retrieval and reasoning capabilities of the models. The framework classifies tasks into Single-Retrieval, Multi-Retrieval, and Multi-Reasoning, reflecting the granular challenges LLMs face in real-world scenarios.

Furthermore, the Ancestral Trace Challenge (ATC) is introduced to simulate complex logical reasoning tasks, pushing the boundaries of LLMs' reasoning abilities. The ATC provides a straightforward yet rigorous method for evaluating LLMs' handling of complex, multi-step logical inferences in extensive contexts.

Experimental Setup and Findings

NeedleBench 4K, 8K, 32K

The paper evaluates several mainstream LLMs across different token lengths on NeedleBench tasks. InternLM2-7B-200K, particularly, stands out in consistent Single-Retrieval performances but faces challenges in Multi-Retrieval tasks, highlighting potential overfitting in single-retrieval scenarios. Qwen-1.5-72B-vLLM, despite its high parameter count, shows unexpected drops in performance when the context length extends toward 1000K tokens, indicating prompt sensitivity and training requirements.

The Mixtral-8x7B Instruct v0.1 model demonstrates superior performance in Multi-Retrieval tasks, suggesting that specific training methods and instructional abilities play a crucial role in effectively handling extensive context lengths.

NeedleBench 1000K

Upon extending the context length to one million tokens, the models assessed, including InternLM2.5-7B-1M and GLM4-9B-Chat-1M, show a significant disparity in performance under different prompt settings. This underscores the inherent prompt-sensitivity in LLMs' long-context capabilities. InternLM2.5-7B-1M, in particular, performs better under most conditions, suggesting a more robust and reliable parameter fine-tuning approach compared to GLM4-9B-Chat-1M.

Ancestral Trace Challenge (ATC)

ATC results further highlight the limitations of current LLMs in real-world long-context logical reasoning tasks. Despite high capabilities demonstrated by leading models like GPT-4 Turbo and Claude 3 in simple retrieval tasks, their effectiveness wanes significantly when faced with complex, multi-step reasoning challenges in long contexts. This is seen consistently across various models, indicating a primary area of development needed in advanced logical reasoning within extensive text contexts.

Practical and Theoretical Implications

The paper's findings have pronounced implications for both practical applications and theoretical advancements:

• Practical: The clear limitations uncovered in current LLMs highlight areas necessitating improvements, particularly in scenarios requiring intricate long-context reasoning. This is critical for fields like law, academia, and business intelligence, where accurate and comprehensive information retrieval and reasoning over long documents are paramount.
• Theoretical: The significant variations in performance due to prompt sensitivity and instruction-following abilities suggest that specific training methodologies and the development of more robust architectures could enhance LLMs' long-context capabilities. The findings advocate for future research to bridge the capability gap, particularly in reasoning tasks involving extensive logical relationships.

Future Developments in AI

Future AI developments, as suggested by this paper, may likely pivot towards addressing the complex requirements of long-context comprehension and reasoning. Improving LLMs to handle multi-step logical tasks, reducing prompt sensitivity, and ensuring robust information retrieval and instructional adherence will be pivotal. Additionally, enhancements in training long-context LLMs to better integrate and synthesize information across extensive texts will be instrumental in advancing their practical utility.

Conclusion

The paper "NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million Context Window?" provides an insightful examination of the long-context retrieval and reasoning capabilities of modern LLMs. While significant strides have been made, the research reveals considerable room for improvement, particularly in practical applications demanding high-stakes, long-context logical reasoning. The detailed and varied evaluations presented in NeedleBench framework underscore a roadmap for future advancements, aiming for more intelligent and adaptive LLMs ready to tackle the complexities of real-world tasks.

The contributions of this paper thus form a cornerstone for future research and development, driving the evolution of LLMs towards more robust, contextually aware, and logically adept AI systems.