Can LLMs reason over extended multilingual contexts? Towards long-context evaluation beyond retrieval and haystacks
MLRBench evaluates LLMs' reasoning over multilingual long contexts, revealing resource gaps.
Key Findings
Methodology
MLRBench uses a synthetic dataset to evaluate LLMs' multilingual long-context reasoning abilities, including tasks like retrieval, multi-hop inference, aggregation, and epistemic reasoning. The dataset spans seven languages, designed to be parallel, leakage-resistant, and scalable to arbitrary context lengths.
Key Results
- Experiments show a significant performance gap between high- and low-resource languages in multi-hop reasoning tasks, with LLMs effectively utilizing less than 30% of their claimed context length.
- Even with off-the-shelf Retrieval Augmented Generation, the long-context problem remains unsolved.
- Comparing retrieval and reasoning tasks, existing benchmarks may overstate LLMs' true long-context reasoning abilities.
Significance
MLRBench provides a more comprehensive evaluation framework for multilingual long-context reasoning, addressing design limitations of existing benchmarks and advancing research and application of multilingual LLMs.
Technical Contribution
MLRBench avoids data leakage through synthetic data generation, task design covers various reasoning abilities, offering a more complete view of LLM evaluation.
Novelty
First to propose a comprehensive evaluation framework for multilingual long-context reasoning, overcoming limitations of traditional retrieval tests.
Limitations
- Performance in low-resource languages needs improvement, especially in complex reasoning tasks.
- Current models effectively utilize less than 30% of their context window for reasoning tasks.
Future Work
Future research could focus on improving reasoning capabilities in low-resource languages and exploring new data generation and evaluation methods.
AI Executive Summary
Existing multilingual long-context benchmarks primarily evaluate a model's ability to locate specific information buried within irrelevant texts, but this retrieval-centric approach is limited. MLRBench uses a synthetic dataset to evaluate LLMs' multilingual long-context reasoning abilities, including tasks like retrieval, multi-hop inference, aggregation, and epistemic reasoning. Experiments show a significant performance gap between high- and low-resource languages in multi-hop reasoning tasks, with LLMs effectively utilizing less than 30% of their claimed context length. Even with off-the-shelf Retrieval Augmented Generation, the long-context problem remains unsolved. MLRBench provides a more comprehensive evaluation framework for multilingual long-context reasoning, addressing design limitations of existing benchmarks and advancing research and application of multilingual LLMs.
Deep Analysis
Background
In recent years, LLMs have made significant progress in processing long input sequences, especially in multilingual settings. However, existing evaluation frameworks mainly focus on retrieval tasks, neglecting reasoning capabilities, leading to overestimation of model abilities.
Core Problem
Existing multilingual long-context benchmarks primarily evaluate a model's ability to locate specific information buried within irrelevant texts, but this retrieval-centric approach is limited and cannot fully assess a model's reasoning capabilities.
Innovation
MLRBench uses a synthetic dataset to evaluate LLMs' multilingual long-context reasoning abilities, including tasks like retrieval, multi-hop inference, aggregation, and epistemic reasoning. The dataset spans seven languages, designed to be parallel, leakage-resistant, and scalable to arbitrary context lengths.
Methodology
- �� Synthetic data generation avoids data leakage.
- �� Task design covers various reasoning abilities.
- �� Dataset scalable to arbitrary context lengths.
Experiments
Experiments conducted with open-weight LLMs across seven languages, evaluating tasks including retrieval, multi-hop inference, aggregation, and epistemic reasoning. Results show a significant performance gap between high- and low-resource languages in multi-hop reasoning tasks.
Results
Experimental results reveal a significant gap between high- and low-resource languages in multi-hop reasoning tasks. LLMs effectively utilize less than 30% of their claimed context length.
Applications
MLRBench can be used to evaluate multilingual LLMs' long-context reasoning abilities, advancing multilingual applications.
Limitations & Outlook
Performance in low-resource languages needs improvement, especially in complex reasoning tasks. Current models effectively utilize less than 30% of their context window for reasoning tasks.
Plain Language Accessible to non-experts
Imagine you're in a library searching for a book. Existing benchmarks are like finding a specific book among many, while MLRBench requires you to understand the content of the book and reason about it. It's like not only finding a book but also understanding the storyline and character relationships.
ELI14 Explained like you're 14
Imagine you're playing a game with many levels. Existing tests are like finding the key to pass a level, while MLRBench requires you not only to find the key but also to understand the story and character relationships in the level. It's like not only passing the level but also understanding the game's plot!
Glossary
Multi-hop Inference
The ability to combine multiple pieces of information for reasoning.
Used in MLRBench to evaluate LLMs' reasoning abilities.
Data Leakage
Overestimation of performance due to the model seeing evaluation data during training.
MLRBench avoids data leakage through synthetic data generation.
Retrieval Augmented Generation
Combining retrieval and generation techniques to improve model performance.
Used to partially alleviate long-context issues but not fully resolve them.
Epistemic Reasoning
The ability to recognize uncertainty or incomplete knowledge and respond appropriately.
Used in MLRBench to evaluate LLMs' reasoning abilities.
Open-source
Publicly released code and datasets for research use.
MLRBench is open-sourced to support future research.
Open Questions Unanswered questions from this research
- 1 How to improve reasoning capabilities in low-resource languages remains an open question.
- 2 Current models effectively utilize less than 30% of their context window for reasoning tasks.
Applications
Immediate Applications
Multilingual LLM Evaluation
MLRBench can be used to evaluate multilingual LLMs' long-context reasoning abilities.
Long-term Vision
Cross-language Applications
Advancing multilingual applications and addressing language resource imbalances.
Abstract
Existing multilingual long-context benchmarks, often based on the popular needle-in-a-haystack test, primarily evaluate a model's ability to locate specific information buried within irrelevant texts. However, such a retrieval-centric approach is myopic and inherently limited, as successful recall alone does not indicate a model's capacity to reason over extended contexts. Moreover, these benchmarks are susceptible to data leakage, short-circuiting, and risk making the evaluation a priori identifiable. To address these limitations, we introduce MLRBench, a new synthetic benchmark for multilingual long-context reasoning. Unlike existing benchmarks, MLRBench goes beyond surface-level retrieval by including tasks that assess multi-hop inference, aggregation, and epistemic reasoning. Spanning seven languages, MLRBench is designed to be parallel, resistant to leakage, and scalable to arbitrary context lengths. Our extensive experiments with an open-weight large language model (LLM) reveal a pronounced gap between high- and low-resource languages, particularly for tasks requiring the model to aggregate multiple facts or predict the absence of information. We also find that, in multilingual settings, LLMs effectively utilize less than 30% of their claimed context length. Although off-the-shelf Retrieval Augmented Generation helps alleviate this to a certain extent, it does not solve the long-context problem. We open-source MLRBench to enable future research in improved evaluation and training of multilingual LLMs.