The CLRS-Text Algorithmic Reasoning Language Benchmark
(2406.04229)Abstract
Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Most recent studies dedicated to reasoning focus on out-of-distribution performance on procedurally-generated synthetic benchmarks, bespoke-built to evaluate specific skills only. This trend makes results hard to transfer across publications, slowing down progress. Three years ago, a similar issue was identified and rectified in the field of neural algorithmic reasoning, with the advent of the CLRS benchmark. CLRS is a dataset generator comprising graph execution traces of classical algorithms from the Introduction to Algorithms textbook. Inspired by this, we propose CLRS-Text -- a textual version of these algorithmic traces. Out of the box, CLRS-Text is capable of procedurally generating trace data for thirty diverse, challenging algorithmic tasks across any desirable input distribution, while offering a standard pipeline in which any additional algorithmic tasks may be created in the benchmark. We fine-tune and evaluate various LMs as generalist executors on this benchmark, validating prior work and revealing a novel, interesting challenge for the LM reasoning community. Our code is available at https://github.com/google-deepmind/clrs/tree/master/clrs/_src/clrs_text.
Overview
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The paper introduces CLRS-Text, a novel benchmarking suite designed to evaluate language models (LMs) on algorithmic reasoning tasks using procedurally generated text traces from classical algorithms.
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The motivation behind this benchmark is to address the limitations observed in current LMs' reasoning capabilities, especially in out-of-distribution scenarios, by leveraging textual traces of algorithms from the 'Introduction to Algorithms' textbook.
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Initial experiments show that models fine-tuned on CLRS-Text, especially the RPE-augmented Gemma 2B model, exhibit marked improvements in reasoning tasks, although challenges remain in extrapolating performance to unseen problem sizes.
The CLRS-Text Algorithmic Reasoning Language Benchmark
Eliciting robust reasoning capabilities from language models (LMs) continues to be a critical direction in developing advanced intelligent systems. The observed performance deficits of LMs in addressing various reasoning tasks necessitate an improved benchmarking approach. "The CLRS-Text Algorithmic Reasoning Language Benchmark" presents a constructive advancement by introducing a standardized and procedurally generated textual dataset aimed at evaluating and enhancing the reasoning abilities of LMs. This essay seeks to provide a formal and detailed overview of this work, its methodology, and its implications for the research community.
Overview
The paper delineates a novel benchmarking suite, CLRS-Text, designed to systematically evaluate LMs on algorithmic reasoning tasks. Unlike existing benchmarks that often rely on static or bespoke datasets, CLRS-Text leverages procedurally generated text traces of classical algorithms drawn from the widely acknowledged "Introduction to Algorithms" textbook. This procedural approach ensures broad coverage of problem instances and mitigates issues related to overfitting and static data-induced illusions of progress.
Motivation and Background
The motivation behind CLRS-Text stems from the observed limitations of current LMs in solving reasoning tasks, particularly when evaluated on out-of-distribution (OOD) datasets. Previous studies have highlighted significant gaps in LMs’ performances on tasks involving elementary arithmetic, logical reasoning, and multi-step planning. To address these, the paper builds on the successful CLRS benchmark, initially engineered for graph neural networks (GNNs), to create a text-based equivalent suitable for LMs. The authors posit that robust algorithmic reasoning can be effectively captured and evaluated using textual traces of polynomial-time algorithms. This is based on the premise that such algorithms encapsulate well-defined, tractable procedures.
Construction of CLRS-Text
CLRS-Text captures algorithmic processes by converting CLRS's graph-based execution traces into text. This transformation enables the generated dataset to be compatible with LMs, facilitating the evaluation of their reasoning capabilities. The benchmark includes traces for thirty classical algorithms categorized under sorting, searching, dynamic programming, and more. Each algorithm's trace involves printing the state of pivotal variables across its execution trajectory, streamlining the analysis and prediction by LMs. The dataset's flexibility allows for generating varied, tailored input distributions, supporting extensive evaluations under both IID and OOD conditions.
Training and Evaluation Framework
For empirical validation, the authors trained a Gemma 2B model variant alongside a randomised positional embeddings (RPE) variant using the CLRS-Text dataset. The evaluation focuses on assessing zero-shot and few-shot performance on unseen problem sizes, replicating practical scenarios where models encounter distributional shifts. These evaluations incorporate resampling techniques to capture the robustness of LMs across multiple runs, avoiding the pitfalls associated with static test data.
The experimental setup juxtaposes these fine-tuned models against general-purpose models like Gemini 1.5 Flash, providing a comparative lens to gauge improvements attributable to bespoke training on CLRS-Text. The models were tested across multiple configurations, with particular attention to their ability to generalize beyond observed training samples.
Results and Implications
The results indicate a marked enhancement in reasoning capabilities for models explicitly trained on CLRS-Text. Particularly, the RPE-augmented Gemma 2B model demonstrated superior generalization performance within the interpolation regime. However, significant challenges remain in the extrapolation regime, where performance gains taper off. This underscores the intrinsic difficulty of length generalization for autoregressive LMs, suggesting potential advantages of non-autoregressive architectures like GNNs for certain reasoning tasks.
The comparative analysis with pre-trained models highlights the substantial potential for targeted fine-tuning, which frontier models could internalize to address complex reasoning tasks more effectively. The incorporation of CLRS-Text as a benchmark not only standardizes the evaluation landscape but also paves the way for coherent and comparable advancements in LM reasoning capabilities.
Future Directions
The study opens several avenues for future research. Primarily, exploring hybrid architectures that blend the strengths of autoregressive and non-autoregressive models might enhance the reasoning capabilities of LMs. Additionally, integrating the benchmark with dynamic evaluation protocols, incorporating continual learning paradigms, and extending the scope to incorporate more diverse algorithmic tasks could further enrich the field.
In conclusion, "The CLRS-Text Algorithmic Reasoning Language Benchmark" provides a significant contribution to the systematic evaluation of LMs' reasoning capabilities. Its procedural nature and alignment with well-established algorithmic principles offer a robust framework for both immediate evaluations and long-term advancements in AI reasoning research.
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