Teaching Large Language Models Number-Focused Headline Generation With Key Element Rationales

• URL: http://arxiv.org/abs/2502.03129v1
• Date: Wed, 05 Feb 2025 12:39:07 GMT
• Title: Teaching Large Language Models Number-Focused Headline Generation With Key Element Rationales
• Authors: Zhen Qian, Xiuzhen Zhang, Xiaofei Xu, Feng Xia,
• Abstract summary: Number-focused headline generation is a unique challenge for Large Language Models (LLMs)<n>We propose a novel chain-of-thought framework for using rationales comprising key elements of the Topic, Entities, and Numerical reasoning (TEN) in news articles.<n>Our approach teaches the student LLM automatic generation of rationales with enhanced capability for numerical reasoning and topic-aligned numerical headline generation.
• Score: 11.428237505896218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Number-focused headline generation is a summarization task requiring both high textual quality and precise numerical accuracy, which poses a unique challenge for Large Language Models (LLMs). Existing studies in the literature focus only on either textual quality or numerical reasoning and thus are inadequate to address this challenge. In this paper, we propose a novel chain-of-thought framework for using rationales comprising key elements of the Topic, Entities, and Numerical reasoning (TEN) in news articles to enhance the capability for LLMs to generate topic-aligned high-quality texts with precise numerical accuracy. Specifically, a teacher LLM is employed to generate TEN rationales as supervision data, which are then used to teach and fine-tune a student LLM. Our approach teaches the student LLM automatic generation of rationales with enhanced capability for numerical reasoning and topic-aligned numerical headline generation. Experiments show that our approach achieves superior performance in both textual quality and numerical accuracy.

Related papers

• Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text [1.7034813545878589]
  COT Fine-tuned is a novel framework for detecting AI-generated text. Key innovation of our method lies in the use of Chain-of-Thought reasoning. Experiments demonstrate that COT Fine-tuned high accuracy in both tasks.
  arXiv  Detail & Related papers  (2025-04-23T17:39:49Z)
• Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification [2.899704155417792]
  We introduce two simple classifier-guided approaches to support counterfactual generation by Large Language Models. Despite their simplicity, our methods outperform state-of-the-art counterfactual generation methods.
  arXiv  Detail & Related papers  (2025-03-06T14:15:07Z)
• Integrating Planning into Single-Turn Long-Form Text Generation [66.08871753377055]
  We propose to use planning to generate long form content. Our main novelty lies in a single auxiliary task that does not require multiple rounds of prompting or planning. Our experiments demonstrate on two datasets from different domains, that LLMs fine-tuned with the auxiliary task generate higher quality documents.
  arXiv  Detail & Related papers  (2024-10-08T17:02:40Z)
• Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization [0.27624021966289597]
  This paper introduces EYEGLAXS, a framework that leverages Large Language Models (LLMs) for extractive summarization. EYEGLAXS focuses on extractive summarization to ensure factual and grammatical integrity. The system sets new performance benchmarks on well-known datasets like PubMed and ArXiv.
  arXiv  Detail & Related papers  (2024-08-28T13:52:19Z)
• Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
  Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement. We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
  arXiv  Detail & Related papers  (2024-07-04T16:41:08Z)
• Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [83.90988015005934]
  Uncertainty quantification is a key element of machine learning applications.<n>We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines.<n>We conduct a large-scale empirical investigation of UQ and normalization techniques across eleven tasks, identifying the most effective approaches.
  arXiv  Detail & Related papers  (2024-06-21T20:06:31Z)
• Exploration of Masked and Causal Language Modelling for Text Generation [6.26998839917804]
  This paper conducts an extensive comparison of Causal Language Modelling approaches for text generation tasks. We first employ quantitative metrics and then perform a qualitative human evaluation to analyse coherence and grammatical correctness. The results show that consistently outperforms CLM in text generation across all datasets.
  arXiv  Detail & Related papers  (2024-05-21T09:33:31Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Exploring the Potential of Large Language Models in Computational Argumentation [54.85665903448207]
  Large language models (LLMs) have demonstrated impressive capabilities in understanding context and generating natural language. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models, and LLaMA2 models, in both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2023-11-15T15:12:15Z)
• Successor Features for Efficient Multisubject Controlled Text Generation [48.37713738712319]
  We introduce SF-GEN, which is grounded in two primary concepts: successor features (SFs) and language model rectification. SF-GEN seamlessly integrates the two to enable dynamic steering of text generation with no need to alter the LLM's parameters. To the best of our knowledge, our research represents the first application of successor features in text generation.
  arXiv  Detail & Related papers  (2023-11-03T00:17:08Z)
• Towards Codable Watermarking for Injecting Multi-bits Information to LLMs [86.86436777626959]
  Large language models (LLMs) generate texts with increasing fluency and realism. Existing watermarking methods are encoding-inefficient and cannot flexibly meet the diverse information encoding needs. We propose Codable Text Watermarking for LLMs (CTWL) that allows text watermarks to carry multi-bit customizable information.
  arXiv  Detail & Related papers  (2023-07-29T14:11:15Z)
• Prompting Large Language Models for Counterfactual Generation: An Empirical Study [13.506528217009507]
  Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks. We present a comprehensive evaluation framework on various types of NLU tasks, which covers all key factors in determining LLMs' capability of generating counterfactuals.
  arXiv  Detail & Related papers  (2023-05-24T06:44:32Z)
• Language Model Self-improvement by Reinforcement Learning Contemplation [13.152789365858812]
  This paper introduces a novel unsupervised method called LanguageModel Self-Improvement by Reinforcement Learning Contemplation (SIRLC) As a student, the model generates answers to unlabeled questions, while as a teacher, it evaluates the generated text and assigns scores accordingly. We demonstrate that SIRLC can be applied to various NLP tasks, such as reasoning problems, text generation, and machine translation.
  arXiv  Detail & Related papers  (2023-05-23T19:25:52Z)

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.