Number Cookbook: Number Understanding of Language Models and How to Improve It

• URL: http://arxiv.org/abs/2411.03766v1
• Date: Wed, 06 Nov 2024 08:59:44 GMT
• Title: Number Cookbook: Number Understanding of Language Models and How to Improve It
• Authors: Haotong Yang, Yi Hu, Shijia Kang, Zhouchen Lin, Muhan Zhang,
• Abstract summary: Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing. This paper comprehensively investigates the numerical understanding and processing ability (NUPA) of LLMs.
• Score: 63.9542740221096
• License:
• Abstract: Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as 9.11 > 9.9). The latter ability is essential for tackling complex arithmetic and mathematical problems and serves as a foundation for most reasoning tasks, but previous work paid little attention to it or only discussed several restricted tasks (like integer addition). In this paper, we comprehensively investigate the numerical understanding and processing ability (NUPA) of LLMs. Firstly, we introduce a benchmark covering four common numerical representations and 17 distinct numerical tasks in four major categories, resulting in 41 meaningful combinations in total. These tasks are derived from primary and secondary education curricula, encompassing nearly all everyday numerical understanding and processing scenarios, and the rules of these tasks are very simple and clear. Through the benchmark, we find that current LLMs fail frequently in many of the tasks. To study the problem, we train small models with existing and potential techniques for enhancing NUPA (such as special tokenizers, PEs, and number formats), comprehensively evaluating their effectiveness using our testbed. We also finetune practical-scale LLMs on our proposed NUPA tasks and find that 1) naive finetuning can improve NUPA a lot on many but not all tasks, and 2) surprisingly, techniques designed to enhance NUPA prove ineffective for finetuning pretrained models. We further explore the impact of chain-of-thought techniques on NUPA. Our work takes a preliminary step towards understanding and improving NUPA of LLMs. Our benchmark and code are released at https://github.com/GraphPKU/number_cookbook.

Related papers

• LLM The Genius Paradox: A Linguistic and Math Expert's Struggle with Simple Word-based Counting Problems [28.72485319617863]
  LLMs struggle with some basic tasks that humans find trivial to handle, e.g., counting the number of character r's in the wordstrawberry. We measure transferability of advanced mathematical and coding reasoning capabilities from specialized LLMs to simple counting tasks. Compared with strategies such as finetuning and in-context learning, we show that engaging reasoning is the most robust and efficient way to help LLMs better perceive tasks.
  arXiv  Detail & Related papers  (2024-10-18T04:17:16Z)
• Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives [54.14429346914995]
  Chain-of-Thought (CoT) has become a pivotal method for solving complex problems. Large language models (LLMs) often struggle to accurately decompose domain-specific tasks. This paper introduces the Re-TASK framework, a novel theoretical model that revisits LLM tasks from the perspectives of capability, skill, and knowledge.
  arXiv  Detail & Related papers  (2024-08-13T13:58:23Z)
• CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model [121.23360004498893]
  We present a benchmark, namely Continual Instruction tuNing (CoIN), to assess existing MLLMs in the sequential instruction tuning paradigm. Experiments on CoIN demonstrate that current powerful MLLMs still suffer catastrophic forgetting. We introduce MoELoRA to MLLMs which is effective to retain the previous instruction alignment.
  arXiv  Detail & Related papers  (2024-03-13T08:54:31Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)
• Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors [11.28397947587596]
  Fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks. However, even advanced instruction-tuned LLMs still fail to outperform small LMs on relation extraction (RE) We propose QA4RE, a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets.
  arXiv  Detail & Related papers  (2023-05-18T17:48:03Z)
• Teaching Algorithmic Reasoning via In-context Learning [45.45116247046013]
  We show that it is possible to teach algorithmic reasoning to large language models (LLMs) via in-context learning. We evaluate our approach on a variety of arithmetic and quantitative reasoning tasks. We achieve an error reduction of approximately 10x, 9x, 5x and 2x respectively compared to the best available baselines.
  arXiv  Detail & Related papers  (2022-11-15T06:12:28Z)
• Large Language Models are Zero-Shot Reasoners [28.6899375595088]
  Chain of thought (CoT) prompting is a technique for eliciting complex multi-step reasoning through step-by-step answer examples. We show that LLMs are decent zero-shot reasoners by simply adding Let's think step by step'' before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances.
  arXiv  Detail & Related papers  (2022-05-24T09:22:26Z)
• Combining Modular Skills in Multitask Learning [149.8001096811708]
  A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume each task is associated with a subset of latent discrete skills from a (potentially small) inventory. We find that the modular design of a network significantly increases sample efficiency in reinforcement learning and few-shot generalisation in supervised learning.
  arXiv  Detail & Related papers  (2022-02-28T16:07:19Z)
• Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model [18.922352061424302]
  We investigate the ability of text-to-text transfer learning model (T5) to learn numeracy. We consider four numeracy tasks: numeration, magnitude order prediction, finding minimum and maximum in a series, and sorting. Although T5 models perform reasonably well in the setting, they struggle considerably in the extrapolation setting across all four tasks.
  arXiv  Detail & Related papers  (2021-09-10T05:33:17Z)
• CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems [56.302581679816775]
  This paper proposes Comprehensive Instruction (CINS) that exploits PLMs with task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD. Experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data.
  arXiv  Detail & Related papers  (2021-09-10T03:23:06Z)

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.