Controllable Citation Sentence Generation with Language Models
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.
Code (1)
Tasks
AttributeLanguage ModelingLanguage ModellingSentenceText GenerationSimilar Papers 제목 키워드 기반
Citation Sentence Generation Leveraging the Content of Cited Papers
We address automatic citation sentence generation, which reduces the burden on writing scientific papers. For highly accurate citation senetence generation, appropriate language must be learned using information such as …
ArticlesSentenceVerifiable Generation with Subsentence-Level Fine-Grained Citations
Verifiable generation requires large language models (LLMs) to cite source documents supporting their outputs, thereby improve output transparency and trustworthiness. Yet, previous work mainly targets the generation of …
SentenceSpecificityALiiCE: Evaluating Positional Fine-grained Citation Generation
Large Language Models (LLMs) can enhance the credibility and verifiability by generating text with citations. However, existing tasks and evaluation methods are predominantly limited to sentence-level statement, neglecti…
SentenceConcise and Sufficient Sub-Sentence Citations for Retrieval-Augmented Generation
In retrieval-augmented generation (RAG) question answering systems, generating citations for large language model (LLM) outputs enhances verifiability and helps users identify potential hallucinations. However, we observ…
Question AnsweringContext-Enhanced Language Models for Generating Multi-Paper Citations
Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers…
Knowledge GraphsSentenceText Generation