Key Findings:
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Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, from natural language processing to mathematical problem-solving and multimodal understanding. However, their reasoning abilities and operational shortcomings have been subject to persistent criticism [1-9]. This article presents a novel approach to evaluating the sequential processing and working memory capabilities of LLMs through a simple yet effective experiment: the Word Swap Challenge.
The Word Swap Challenge represents the simplest form of logical reasoning with order-sensitive and causal dependencies. This concept extends to more complex scenarios encountered in daily life, such as following a narrative or solving a crime by examining evidence in a specific sequence. The ability to correctly handle these dependencies is crucial in most situations and is a skill humans develop from a young age. For instance, consider a simple instruction like “Check if we have milk at home. If not, buy some. And pick up cereal if you buy milk, otherwise bread will do.” – a task that requires understanding sequential logic and conditional decision-making.
Our experience in developing agents with Long-Term Memory (LTM) and integrating them into video games has highlighted notable constraints in LLMs’ abilities to integrate and process multiple pieces of information in a specific order—a capability essential for sophisticated reasoning. To systematically evaluate and quantify these limitations, we conducted a study focusing on LLMs’ capacity to perform operations that require precise information integration.
Our experiments reveal that even with this relatively simple task of word substitutions, most LLMs begin to falter after just two sequential operations, with accuracy dropping sharply as the number of operations increases. This exposes a fundamental weakness in LLMs’ ability to handle tasks requiring interdependent sequential reasoning, contrasting with their strong performance in other areas.
These results have important implications for understanding the limitations of LLMs in iterative reasoning tasks. They provide a swift, effective method for assessing an LLM’s cognitive-like abilities, particularly in scenarios that demand precise order-sensitive and causal dependencies. This research contributes to the ongoing discussion about the true capabilities and limitations of LLMs in complex reasoning tasks.
The Word Swap Challenge: Pushing LLMs to Their Processing Limits
This challenge presents an LLM with a list of five ordinary words, and tasks it with performing a series of word swaps, each building upon the last. It’s akin to a high-stakes game of verbal Jenga, where one misstep can bring the whole structure tumbling down. This deceptively straightforward task pushes language models to their cognitive limits.
We have identified a compelling correlation: an LLM’s performance on this task serves as a remarkably accurate predictor of its overall cognitive abilities, particularly within the context of our systems, that frequently require interdependent reasoning. This discovery has led us to adopt the challenge as part of our evaluation toolbox, functioning as a litmus test for assessing new language models prior to their deeper evaluation.
The test’s power lies in its ability to isolate and measure an LLM’s capacity for interdependent sequential reasoning, a fundamental aspect of reasoning. By stripping away the complexities of natural language and focusing on pure sequential operations, we can gauge an LLM’s working memory, attention to detail, and ability to follow multi-step instructions accurately. These skills are crucial for LLMs’ applicability in real-world applications, making the Word Swap Challenge a light-weight, yet invaluable benchmark in our ongoing research and development efforts.
Here’s what makes the Word Swap Challenge so compelling:
- It isolates pure sequential processing, free from the noise of complex language or vast knowledge bases.
- It reveals how many interconnected steps an LLM can juggle in its ‘mental workspace’.
- It exposes the delicate balance between an LLM’s short-term memory and its ability to apply rules consistently.
As we’ll see, even the most advanced LLMs can stumble when the chain of operations grows too long. It’s a humbling reminder that beneath their seemingly boundless knowledge, these models still grapple with the fundamentals of logical thinking.
Example prompt
The prompts we craft are short and straightforward, with careful attention to formatting and word choice. Specifically, we ensure that:
- the words used are common and familiar.
- each word is represented by a single token.
This approach allows us to focus solely on the model’s sequential reasoning processes, eliminating factors like tokenization artifacts or mid-sentence misunderstandings. For instance, in the example above, the expected output list is [rush;night;ghost;chain;population].
Results
Our experiments encompass a range of 1 to 30 switch operations, with each configuration tested 10 times to evaluate the consistency of correct responses from each LLM. We deliberately adopt a lenient approach to output formatting to isolate and assess the models’ core reasoning capabilities. This flexibility in formatting doesn’t affect the performance of more advanced LLMs, though some less sophisticated models occasionally struggle with precise output structure.
To ensure a comprehensive evaluation, we tested a diverse array of LLMs, including various versions of closed-weight models (o-1, GPT-4 Turbo, GPT-4o, and other variants by OpenAI, Claude Opus and Sonnet), as well as some prominent open-weight language models, such as LLaMA 3 and Gemma 2. This broad selection allows us to draw meaningful comparisons across different model architectures and training approaches. We evaluate each model using two temperature configurations: low (0) and high (1), then select the best result from these two settings (please note that the plots displaying results for each temperature individually are not arranged by AuC; the model order corresponds to the order in the combined graph). This is important because some models perform better under less greedy sampling strategies.
Surprisingly, this seemingly straightforward task proves to be a formidable challenge for the LLMs. The majority of models begin to err after just 2 operations, and only a handful maintain reasonable accuracy beyond 5 operations. OpenAI’s o1-mini emerges as the top performer, followed by Claude 3 Opus and 3.5 Sonnet, yet both Opus and Sonnet still struggle to surpass the 10-operation threshold without errors. Meanwhile, both the older GPT-4 and GPT-4 Turbo models continue to perform robustly, outperforming their newer 4o alternatives.
The results from o1-mini may initially suggest that it outperforms other models in interdependent sequential reasoning, but in reality this might not necessarily be the case. Our Word Swap Challenge specifically evaluates one-shot responses, whereas the Chain-of-Thought (CoT) [10] approach, which o1-mini is understood to exploit, circumvents the challenge by breaking down the instructions into their obvious intermediate steps and rolling out all partial solutions. While this might do the job for a proxy task like this, other less-structured forms of reasoning —related to abstraction and deduction— will ultimately reveal the true reasoning power of the model. Therefore these results highlight the importance of being aware of the limitations of the tests that we rely on, and interpreting and extending those accordingly.
A particularly intriguing observation is the pronounced performance dip occurring at the 4-operation mark across 5 of the models tested. A deeper analysis suggests that this dip is linked to an increased rate of word reinstatements, where a word is replaced and then reintroduced into the list. This pattern presents a significant challenge for most models, with the older GPT-4 versions exhibiting the most severe impact.
Conclusions
These findings highlight a critical vulnerability in current LLMs: their difficulty with prompts that demand sequential thinking. Despite their capacity to handle inputs spanning thousands of tokens, our tests utilize fewer than 340 tokens and a simple word-swap procedure, demonstrating that the challenge lies not in the volume of input but in the complexity of the processing or reasoning required. We believe these insights enhance our understanding of the limitations in LLMs’ logical processing and provide a swift method for assessing an LLM’s reasoning abilities. It is thanks to those reasoning abilities that an LLM is capable of generalizing to novel scenarios where it hasn’t developed pre-learned reasoning shortcuts.
Looking ahead, we aim to delve deeper into the impact of compounding errors, such as those previously discussed, by pinpointing specific cases that present unique challenges to LLMs. Additionally, we plan to investigate how varying prompt lengths affect accuracy rates, thereby gaining a more comprehensive view of how prompt size influences logical reasoning performance. Last but not least, we will explore how standard CoT improves performance of LLMs on the Word Swap Challenge and whether a new variant of CoT, inspired by the challenge, could approach the performance of the best performing LLM in the study, namely o1-mini. One final thought to ponder: Does this challenge tell us something about how to train our models to be better reasoners? If you have some thoughts or any other questions regarding this and similar topics, join us on discord.
You can access the code for these experiments in our GitHub repository, and view the results through this link.
References
[1] Kambhampati, S. (2024). Can large language models reason and plan? arXiv preprint arXiv:2403.04121. Retrieved from https://arxiv.org/abs/2403.04121
[2] Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., & Chen, H. (2023). Reasoning with language model prompting: A survey. arXiv preprint arXiv:2212.09597. Retrieved from https://arxiv.org/abs/2212.09597
[3] Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2024). Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks. NAACL 2024. doi:10.18653/v1/2024.naacl-long.102, link
[4] Wang, Z., Fan, W., Zong, Q., Zhang, H., Choi, S., Fang, T., Liu, X., Song, Y., Wong, G. Y., & See, S. (2024). Inductive or deductive? Rethinking the fundamental reasoning abilities of LLMs. arXiv preprint arXiv:2408.00114. Retrieved from https://arxiv.org/abs/2408.00114
[5] Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2024). Do large language models reason like us? PNAS Nexus, 3(7), page 233. doi:10.1093/pnasnexus/pgae233, link
[6] Nezhurina, M., Cipolina-Kun, L., Cherti, M. and Jitsev, J., 2024. Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models. arXiv preprint arXiv:2406.02061. Retrieved from https://arxiv.org/abs/2406.02061
[7] Stechly, K., Valmeekam, K., & Kambhampati, S. (2024). Chain of thoughtlessness: An analysis of cot in planning. arXiv preprint arXiv:2405.04776. Retrieved from https://arxiv.org/abs/2405.04776
[8] Mirzadeh, I., Alizadeh, K., Shahrokhi, H., Tuzel, O., Bengio, S. and Farajtabar, M., 2024. GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models. arXiv preprint arXiv:2410.05229. Retrieved from https://arxiv.org/abs/2410.05229
[9] Hosseini, A., Sordoni, A., Toyama, D., Courville, A. and Agarwal, R., 2024. Not All LLM Reasoners Are Created Equal. arXiv preprint arXiv:2410.01748. Retrieved from https://arxiv.org/abs/2410.01748
[10] Wei J., Wang X., Schuurmans D., Bosma M., Ichter B., Xia F., Chi E.H., Le Q.V., and Zhou D. 2024. Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS ’22). Article 1800, 24824–24837. link
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