LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages (2406.06196v3)

Abstract: In this paper, we present the LingOly benchmark, a novel benchmark for advanced reasoning abilities in LLMs. Using challenging Linguistic Olympiad puzzles, we evaluate (i) capabilities for in-context identification and generalisation of linguistic patterns in very low-resource or extinct languages, and (ii) abilities to follow complex task instructions. The LingOly benchmark covers more than 90 mostly low-resource languages, minimising issues of data contamination, and contains 1,133 problems across 6 formats and 5 levels of human difficulty. We assess performance with both direct accuracy and comparison to a no-context baseline to penalise memorisation. Scores from 11 state-of-the-art LLMs demonstrate the benchmark to be challenging, and models perform poorly on the higher difficulty problems. On harder problems, even the top model only achieved 38.7% accuracy, a 24.7% improvement over the no-context baseline. Large closed models typically outperform open models, and in general, the higher resource the language, the better the scores. These results indicate, in absence of memorisation, true multi-step out-of-domain reasoning remains a challenge for current LLMs.

An Analysis of the LingOly Benchmark for Linguistic Reasoning in LLMs

The paper "LingOly: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages" presents the LingOly benchmark, a novel framework for evaluating advanced reasoning capabilities in LLMs. The LingOly benchmark utilizes a range of challenging Linguistic Olympiad puzzles to assess model performance, focusing on very low-resource or extinct languages and complex instruction-following tasks. This benchmark is significant in its inclusion of over 90 mostly low-resource languages and its composition of 1,133 problems across diverse formats and five levels of human difficulty.

Key Components of the LingOly Benchmark

The paper introduces several critical aspects of the LingOly benchmark that differentiate it from existing benchmarks:

• Linguistic Scope: The benchmark includes puzzles from over 90 languages, many of which are low-resource or extinct, reducing the likelihood of data contamination and ensuring that models are assessed on true reasoning abilities rather than memorization.
• Problem Diversity: The benchmark encompasses six formats—Rosetta, Pattern, Computational, Monolingual Text, Match-up, and Text—each requiring different forms of pattern identification and instruction following.
• Difficulty Levels: The puzzles are categorized into five human difficulty levels, from Breakthrough (for young children) to Round 2 (for advanced participants).

Evaluation Metrics

Performance is evaluated using both exact match accuracy and improvement over a no-context baseline. The no-context baseline attempts to mitigate the impact of data memorization by removing the context provided in the problem, challenging the model to demonstrate true reasoning capabilities rather than retrieval of known answers.

Model Performance Analysis

The paper assessed 11 state-of-the-art LLMs, including both open models (e.g., Llama 3, Mixtral 8x7B) and closed models (e.g., GPT-4, Claude Opus). Key findings indicate significant room for improvement in LLM performance, especially on more difficult problems:

• Exact Match Scores: The benchmark proved challenging, with an average exact match score of only 21.8% across all models, and higher scores for easier problems compared to harder ones.
• Top Performers: Claude Opus emerged as the top performer with 46.3% exact match accuracy, whereas Mixtral 8x7B was the best among open models, albeit with a considerably lower score of 14.2% accuracy.
• Impact of Language Resourcing: Performance was generally higher for high-resource languages, suggesting that current models benefit significantly from extensive pre-training on well-represented languages. Nevertheless, even adjusting for data contamination, models like Claude Opus demonstrated relatively better out-of-context reasoning capabilities.

Key Findings and Implications

The research reveals that despite advancements in LLMs, multi-step reasoning in low-resource languages remains a significant challenge. Key limitations identified include:

• Instruction Following: Some lower performance can be attributed to auxiliary tasks like the complex instruction-following inherent in these puzzles.
• Memorization Risks: The no-context baseline helps isolate true reasoning abilities by showing that a substantial proportion of correct answers in easier puzzles could be due to memorization.

Theoretical and Practical Implications

From a theoretical standpoint, the research underscores the need for more sophisticated evaluation frameworks that can adequately differentiate between memorization and reasoning. Practically, the findings demonstrate the importance of focusing on underrepresented languages in model training and benchmarking to achieve more robust and generalizable LLMs.

Future Directions

Future work could extend LingOly to include multimodal tasks, as current benchmarks are limited to text-based reasoning. Additionally, integrating nuanced partial credit scoring, similar to human evaluations in Linguistic Olympiads, could provide a more granular measurement of model capabilities. Further exploration into fine-tuning models on low-resource settings may enhance performance in these challenging scenarios.

In conclusion, the LingOly benchmark provides a valuable and rigorous tool for advancing the evaluation of linguistic reasoning abilities in LLMs, highlighting current capabilities and identifying critical areas for future improvement. The research contributes significantly to our understanding of how well these models can handle complex linguistic tasks in diverse and underrepresented languages.