Do LLMs Implicitly Determine the Suitable Text Difficulty for Users?

• URL: http://arxiv.org/abs/2402.14453v1
• Date: Thu, 22 Feb 2024 11:16:23 GMT
• Title: Do LLMs Implicitly Determine the Suitable Text Difficulty for Users?
• Authors: Seiji Gobara, Hidetaka Kamigaito and Taro Watanabe
• Abstract summary: We show that large language models can implicitly handle text difficulty between user input and generated text. Some LLMs can surpass humans in handling text difficulty and the importance of instruction-tuning.
• Score: 29.6000895693808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Education that suits the individual learning level is necessary to improve students' understanding. The first step in achieving this purpose by using large language models (LLMs) is to adjust the textual difficulty of the response to students. This work analyzes how LLMs can implicitly adjust text difficulty between user input and its generated text. To conduct the experiments, we created a new dataset from Stack-Overflow to explore the performance of question-answering-based conversation. Experimental results on the Stack-Overflow dataset and the TSCC dataset, including multi-turn conversation show that LLMs can implicitly handle text difficulty between user input and its generated response. We also observed that some LLMs can surpass humans in handling text difficulty and the importance of instruction-tuning.

Related papers

• Which LLMs are Difficult to Detect? A Detailed Analysis of Potential Factors Contributing to Difficulties in LLM Text Detection [43.66875548677324]
  We train AI-generated (AIG) text classifiers using the LibAUC library for training classifiers with imbalanced datasets. Our results in the Deepfake Text dataset show that AIG-text detection varies across domains, with scientific writing being relatively challenging. In the Rewritten Ivy Panda dataset focusing on student essays, we find that the OpenAI family of LLMs was substantially difficult for our classifiers to distinguish from human texts.
  arXiv  Detail & Related papers  (2024-10-18T21:42:37Z)
• CUTE: Measuring LLMs' Understanding of Their Tokens [54.70665106141121]
  Large Language Models (LLMs) show remarkable performance on a wide variety of tasks. This raises the question: To what extent can LLMs learn orthographic information? We propose a new benchmark, which features a collection of tasks designed to test the orthographic knowledge of LLMs.
  arXiv  Detail & Related papers  (2024-09-23T18:27:03Z)
• Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts [20.933548500888595]
  Using large language models (LLMs) for educational applications like dialogue-based teaching is a hot topic. Current Static metrics for text difficulty, like the Flesch-Kincaid Reading Ease score, are known to be crude and brittle. We introduce and evaluate a new set of Prompt-based metrics for text difficulty.
  arXiv  Detail & Related papers  (2024-05-15T16:22:16Z)
• When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models [59.84769254832941]
  We propose a FaLlacy Understanding Benchmark (FLUB) containing cunning texts that are easy for humans to understand but difficult for models to grasp. Specifically, the cunning texts that FLUB focuses on mainly consist of the tricky, humorous, and misleading texts collected from the real internet environment. Based on FLUB, we investigate the performance of multiple representative and advanced LLMs.
  arXiv  Detail & Related papers  (2024-02-16T22:12:53Z)
• INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning [59.07490387145391]
  Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. Their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. We introduce a novel instruction tuning dataset, INTERS, encompassing 20 tasks across three fundamental IR categories.
  arXiv  Detail & Related papers  (2024-01-12T12:10:28Z)
• Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions [19.365615476223635]
  Conversational question-answering systems aim to create interactive search systems that retrieve information by interacting with users. Existing work uses human annotators to play the roles of the questioner (student) and the answerer (teacher) We propose a simulation framework that employs zero-shot learner LLMs for simulating teacher-student interactions.
  arXiv  Detail & Related papers  (2023-12-05T17:38:02Z)
• How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection [39.254432080406346]
  Even task-oriented constraints -- constraints that would naturally be included in an instruction and are not related to detection-evasion -- cause existing powerful detectors to have a large variance in detection performance. Our experiments show that the standard deviation (SD) of current detector performance on texts generated by an instruction with such a constraint is significantly larger (up to an SD of 14.4 F1-score) than that by generating texts multiple times or paraphrasing the instruction.
  arXiv  Detail & Related papers  (2023-11-14T18:32:52Z)
• MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning [63.80739044622555]
  We introduce MuSR, a dataset for evaluating language models on soft reasoning tasks specified in a natural language narrative. This dataset has two crucial features. First, it is created through a novel neurosymbolic synthetic-to-natural generation algorithm. Second, our dataset instances are free text narratives corresponding to real-world domains of reasoning.
  arXiv  Detail & Related papers  (2023-10-24T17:59:20Z)
• SeqXGPT: Sentence-Level AI-Generated Text Detection [62.3792779440284]
  We introduce a sentence-level detection challenge by synthesizing documents polished with large language models (LLMs) We then propose textbfSequence textbfX (Check) textbfGPT, a novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection.
  arXiv  Detail & Related papers  (2023-10-13T07:18:53Z)
• Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering [9.158919909909146]
  In this paper, we propose a novel way of employing labeled data such as it informs LLM of some undesired output. Experiments on three multi-span question answering datasets and a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM's in-context learning performance.
  arXiv  Detail & Related papers  (2023-06-07T15:20:24Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.