Controllable Citation Sentence Generation with Language Models

• URL: http://arxiv.org/abs/2211.07066v2
• Date: Thu, 14 Dec 2023 16:13:15 GMT
• Title: Controllable Citation Sentence Generation with Language Models
• Authors: Nianlong Gu, Richard H.R. Hahnloser
• Abstract summary: We propose to integrate the manuscript context, the context of the referenced paper, and the desired control attributes into a structured template and use it to fine-tune a language model (LM) via next-token prediction. The proposed workflow harmoniously combines citation attribute suggestion and conditional citation generation into one LM, allowing for better user control.
• Score: 11.186252009101077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Citation generation aims to generate a citation sentence that refers to a chosen paper in the context of a manuscript. However, a rigid citation generation process is at odds with an author's desire to control specific attributes, such as 1) the citation intent, e.g., either introducing background information or comparing results, and 2) keywords that should appear in the citation text. To provide these degrees of controllability during citation generation, we propose to integrate the manuscript context, the context of the referenced paper, and the desired control attributes into a structured template and use it to fine-tune a language model (LM) via next-token prediction. We then utilize Proximal Policy Optimization to directly optimize the LM in favor of a high score of our proposed controllability metric. The proposed workflow harmoniously combines citation attribute suggestion and conditional citation generation into one LM, allowing for better user control.

Related papers

• Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation [51.8188846284153]
  RAG has been widely adopted to enhance Large Language Models (LLMs) Attributed Text Generation (ATG) has attracted growing attention, which provides citations to support the model's responses in RAG. This paper proposes a fine-grained ATG method called ReClaim(Refer & Claim), which alternates the generation of references and answers step by step.
  arXiv  Detail & Related papers  (2024-07-01T20:47:47Z)
• ALiiCE: Evaluating Positional Fine-grained Citation Generation [54.19617927314975]
  We propose ALiiCE, the first automatic evaluation framework for fine-grained citation generation. Our framework first parses the sentence claim into atomic claims via dependency analysis and then calculates citation quality at the atomic claim level. We evaluate the positional fine-grained citation generation performance of several Large Language Models on two long-form QA datasets.
  arXiv  Detail & Related papers  (2024-06-19T09:16:14Z)
• Verifiable Generation with Subsentence-Level Fine-Grained Citations [13.931548733211436]
  Verifiable generation requires large language models to cite source documents supporting their outputs. Previous work mainly targets the generation of sentence-level citations, lacking specificity about which parts of a sentence are backed by the cited sources. This work studies verifiable generation with subsentence-level fine-grained citations for more precise location of generated content supported by the cited sources.
  arXiv  Detail & Related papers  (2024-06-10T09:32:37Z)
• Contextualizing Generated Citation Texts [11.531517736126657]
  We propose a simple modification to the citation text generation task. The generation target is not only the citation itself, but the entire context window, including the target citation.
  arXiv  Detail & Related papers  (2024-02-28T05:24:21Z)
• Generative Context-aware Fine-tuning of Self-supervised Speech Models [54.389711404209415]
  We study the use of generative large language models (LLM) generated context information. We propose an approach to distill the generated information during fine-tuning of self-supervised speech models. We evaluate the proposed approach using the SLUE and Libri-light benchmarks for several downstream tasks: automatic speech recognition, named entity recognition, and sentiment analysis.
  arXiv  Detail & Related papers  (2023-12-15T15:46:02Z)
• A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation [67.98367574025797]
  Existing syntactically-controlled paraphrase generation models perform promisingly with human-annotated or well-chosen syntactic templates. The prohibitive cost makes it unfeasible to manually design decent templates for every source sentence. We propose a novel Quality-based Syntactic Template Retriever (QSTR) to retrieve templates based on the quality of the to-be-generated paraphrases.
  arXiv  Detail & Related papers  (2023-10-20T03:55:39Z)
• Towards generating citation sentences for multiple references with intent control [86.53829532976303]
  We build a novel generation model with the Fusion-in-Decoder approach to cope with multiple long inputs. Experiments demonstrate that the proposed approaches provide much more comprehensive features for generating citation sentences.
  arXiv  Detail & Related papers  (2021-12-02T15:32:24Z)
• Learning Neural Textual Representations for Citation Recommendation [7.227232362460348]
  We propose a novel approach to citation recommendation using a deep sequential representation of the documents (Sentence-BERT) cascaded with Siamese and triplet networks in a submodular scoring function. To the best of our knowledge, this is the first approach to combine deep representations and submodular selection for a task of citation recommendation.
  arXiv  Detail & Related papers  (2020-07-08T12:38:50Z)
• Context-Based Quotation Recommendation [60.93257124507105]
  We propose a novel context-aware quote recommendation system. It generates a ranked list of quotable paragraphs and spans of tokens from a given source document. We conduct experiments on a collection of speech transcripts and associated news articles.
  arXiv  Detail & Related papers  (2020-05-17T17:49:53Z)

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.