One Arrow, Many Targets: Probing LLMs for Multi-Attribute Controllable Text Summarization

• URL: http://arxiv.org/abs/2411.01213v1
• Date: Sat, 02 Nov 2024 11:07:25 GMT
• Title: One Arrow, Many Targets: Probing LLMs for Multi-Attribute Controllable Text Summarization
• Authors: Tathagato Roy, Rahul Mishra,
• Abstract summary: Multi-Attribute Controllable Summarization (MACS) is a well-established task within the natural language processing (NLP) community. This work addresses the gap by examining the MACS task through the lens of large language models. We propose and evaluate a novel hierarchical adapter fusion technique to integrate learnings from two distinct controllable attributes.
• Score: 7.734726150561089
• License:
• Abstract: Text summarization is a well-established task within the natural language processing (NLP) community. However, the focus on controllable summarization tailored to user requirements is gaining traction only recently. While several efforts explore controllability in text summarization, the investigation of Multi-Attribute Controllable Summarization (MACS) remains limited. This work addresses this gap by examining the MACS task through the lens of large language models (LLMs), using various learning paradigms, particularly low-rank adapters. We experiment with different popular adapter fine-tuning strategies to assess the effectiveness of the resulting models in retaining cues and patterns associated with multiple controllable attributes. Additionally, we propose and evaluate a novel hierarchical adapter fusion technique to integrate learnings from two distinct controllable attributes. Subsquently, we present our findings, discuss the challenges encountered, and suggest potential avenues for advancing the MACS task.

Related papers

• Guide-to-Explain for Controllable Summarization [11.904090197598505]
  controllable summarization with large language models (LLMs) remains underexplored. We propose a guide-to-explain framework (GTE) for controllable summarization. Our framework enables the model to identify misaligned attributes in the initial draft and guides it in explaining errors in the previous output.
  arXiv  Detail & Related papers  (2024-11-19T12:36:02Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Exploring the Transferability of Visual Prompting for Multimodal Large Language Models [47.162575147632396]
  Transferable Visual Prompting (TVP) is a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model. We introduce two strategies to address the issue of cross-model feature corruption of existing visual prompting methods and enhance the transferability of the learned prompts.
  arXiv  Detail & Related papers  (2024-04-17T09:39:07Z)
• Meta-Task Prompting Elicits Embeddings from Large Language Models [54.757445048329735]
  We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation. We generate high-quality sentence embeddings from Large Language Models without the need for model fine-tuning. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
  arXiv  Detail & Related papers  (2024-02-28T16:35:52Z)
• Prompt Highlighter: Interactive Control for Multi-Modal LLMs [50.830448437285355]
  This study targets a critical aspect of multi-modal LLMs' (LLMs&VLMs) inference: explicit controllable text generation. We introduce a novel inference method, Prompt Highlighter, which enables users to highlight specific prompt spans to interactively control the focus during generation. We find that, during inference, guiding the models with highlighted tokens through the attention weights leads to more desired outputs.
  arXiv  Detail & Related papers  (2023-12-07T13:53:29Z)
• 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)
• SEMQA: Semi-Extractive Multi-Source Question Answering [94.04430035121136]
  We introduce a new QA task for answering multi-answer questions by summarizing multiple diverse sources in a semi-extractive fashion. We create the first dataset of this kind, QuoteSum, with human-written semi-extractive answers to natural and generated questions.
  arXiv  Detail & Related papers  (2023-11-08T18:46:32Z)
• Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards [42.171703872560286]
  Controllability is a matter of concern when it comes to text generation tasks with long inputs, such as multi-document summarization. We train a controllable content extraction scheme to extract the text that will be refined by an LLM. Our approach yields competitive results in the evaluation using ROUGE metrics and outperforms potential baselines in coherence.
  arXiv  Detail & Related papers  (2023-10-05T11:29:09Z)
• On the Performance of Multimodal Language Models [4.677125897916577]
  This study conducts a comparative analysis of different multimodal instruction tuning approaches. We reveal key insights for guiding architectural choices when incorporating multimodal capabilities into large language models.
  arXiv  Detail & Related papers  (2023-10-04T23:33:36Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space [110.85888003111653]
  Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously. We introduce a novel approach for multi-aspect control, namely MacLaSa, that estimates compact latent space for multiple aspects. We show that MacLaSa outperforms several strong baselines on attribute relevance and textual quality while maintaining a high inference speed.
  arXiv  Detail & Related papers  (2023-05-22T07:30:35Z)

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.