IOLBENCH: Benchmarking LLMs on Linguistic Reasoning

• URL: http://arxiv.org/abs/2501.04249v1
• Date: Wed, 08 Jan 2025 03:15:10 GMT
• Title: IOLBENCH: Benchmarking LLMs on Linguistic Reasoning
• Authors: Satyam Goyal, Soham Dan,
• Abstract summary: We introduce IOLBENCH, a novel benchmark derived from International Linguistics Olympiad (IOL) problems.<n>This dataset encompasses diverse problems testing syntax, morphology, phonology, and semantics.<n>We find that even the most advanced models struggle to handle the intricacies of linguistic complexity.
• Score: 8.20398036986024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the remarkable advancements and widespread applications of deep neural networks, their ability to perform reasoning tasks remains limited, particularly in domains requiring structured, abstract thought. In this paper, we investigate the linguistic reasoning capabilities of state-of-the-art large language models (LLMs) by introducing IOLBENCH, a novel benchmark derived from International Linguistics Olympiad (IOL) problems. This dataset encompasses diverse problems testing syntax, morphology, phonology, and semantics, all carefully designed to be self-contained and independent of external knowledge. These tasks challenge models to engage in metacognitive linguistic reasoning, requiring the deduction of linguistic rules and patterns from minimal examples. Through extensive benchmarking of leading LLMs, we find that even the most advanced models struggle to handle the intricacies of linguistic complexity, particularly in areas demanding compositional generalization and rule abstraction. Our analysis highlights both the strengths and persistent limitations of current models in linguistic problem-solving, offering valuable insights into their reasoning capabilities. By introducing IOLBENCH, we aim to foster further research into developing models capable of human-like reasoning, with broader implications for the fields of computational linguistics and artificial intelligence.

Related papers

• Linguistic Blind Spots of Large Language Models [14.755831733659699]
  We study the performance of recent large language models (LLMs) on linguistic annotation tasks. We find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. Our results provide insights to inform future advancements in LLM design and development.
  arXiv  Detail & Related papers  (2025-03-25T01:47:13Z)
• A Survey on Post-training of Large Language Models [185.51013463503946]
  Large Language Models (LLMs) have fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration. These challenges necessitate advanced post-training language models (PoLMs) to address shortcomings, such as restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance. This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms.
  arXiv  Detail & Related papers  (2025-03-08T05:41:42Z)
• Sparse Auto-Encoder Interprets Linguistic Features in Large Language Models [40.12943080113246]
  We present a systematic and comprehensive causal investigation using sparse auto-encoders (SAEs) We extract a wide range of linguistic features from six dimensions. We introduce two indices-Feature Representation Confidence (FRC) and Feature Intervention Confidence (FIC)-to measure the ability of linguistic features to capture and control linguistic phenomena.
  arXiv  Detail & Related papers  (2025-02-27T18:16:47Z)
• Probing Large Language Models in Reasoning and Translating Complex Linguistic Puzzles [0.6144680854063939]
  This paper investigates the utilization of Large Language Models (LLMs) for solving complex linguistic puzzles. Using datasets from the Puzzling Machine Competition and various Linguistics Olympiads, we employ a comprehensive set of metrics to assess the performance of GPT-4 0603.
  arXiv  Detail & Related papers  (2025-02-02T14:53:14Z)
• A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [51.8203871494146]
  The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient. This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
  arXiv  Detail & Related papers  (2024-05-17T17:47:39Z)
• Scalable Language Model with Generalized Continual Learning [58.700439919096155]
  The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
  arXiv  Detail & Related papers  (2024-04-11T04:22:15Z)
• Unveiling A Core Linguistic Region in Large Language Models [49.860260050718516]
  This paper conducts an analogical research using brain localization as a prototype. We have discovered a core region in large language models that corresponds to linguistic competence. We observe that an improvement in linguistic competence does not necessarily accompany an elevation in the model's knowledge level.
  arXiv  Detail & Related papers  (2023-10-23T13:31:32Z)
• Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
  Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
  arXiv  Detail & Related papers  (2023-10-02T01:00:50Z)
• Re-Reading Improves Reasoning in Large Language Models [87.46256176508376]
  We introduce a simple, yet general and effective prompting method, Re2, to enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs) Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. We evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.
  arXiv  Detail & Related papers  (2023-09-12T14:36:23Z)
• Large Language Models Are Not Strong Abstract Reasoners [12.354660792999269]
  Large Language Models have shown tremendous performance on a variety of natural language processing tasks. It is unclear whether LLMs can achieve human-like cognitive capabilities or whether these models are still fundamentally circumscribed. We introduce a new benchmark for evaluating language models beyond memorization on abstract reasoning tasks.
  arXiv  Detail & Related papers  (2023-05-31T04:50:29Z)
• Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners [75.85554779782048]
  Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned textitsemantics of language tokens do the most heavy lifting during the reasoning process.
  arXiv  Detail & Related papers  (2023-05-24T07:33:34Z)
• Emergent Linguistic Structures in Neural Networks are Fragile [20.692540987792732]
  Large Language Models (LLMs) have been reported to have strong performance on natural language processing tasks. We propose a framework to assess the consistency and robustness of linguistic representations.
  arXiv  Detail & Related papers  (2022-10-31T15:43:57Z)
• Shortcut Learning of Large Language Models in Natural Language Understanding [119.45683008451698]
  Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. They might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly affected their generalizability and adversarial robustness.
  arXiv  Detail & Related papers  (2022-08-25T03:51:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.