Pith. sign in

REVIEW 6 cited by

Sentence Simplification via Large Language Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2302.11957 v1 pith:V2D5Z37F submitted 2023-02-23 cs.CL cs.AI

Yutao Feng , Jipeng Qiang , Yun Li , Yunhao Yuan , Yi Zhu This is my paper · ORCID
classification cs.CLcs.AI
keywords llmssentencesimplificationlanguageabilitylargemodelssentences
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning. Large Language models (LLMs) have demonstrated the ability to perform a variety of natural language processing tasks. However, it is not yet known whether LLMs can be served as a high-quality sentence simplification system. In this work, we empirically analyze the zero-/few-shot learning ability of LLMs by evaluating them on a number of benchmark test sets. Experimental results show LLMs outperform state-of-the-art sentence simplification methods, and are judged to be on a par with human annotators.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Progressive Document-level Text Simplification via Large Language Models

    cs.CL 2025-01 conditional novelty 6.0 of 10

    A three-stage hierarchical LLM pipeline for document simplification outperforms direct ChatGPT prompts and earlier methods on Wiki-auto and Newsela, with caveats about self-evaluation.

  2. A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences

    cs.CL 2025-06 reject novelty 5.0 of 10

    A multi-agent GPT-4O pipeline with an internal semantic-lexical gate claims 70% success on simplifying 100 video game sentences, versus 48% for a single-agent version.

  3. Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Adding all missing named entities back into simplified biomedical text yields the highest cosine similarity and ROUGE-1 to the original among five insertion strategies, but the evaluation is partly circular and lacks ...

  4. From Words to Workflows: Automating Business Processes

    cs.AI 2024-12 conditional novelty 5.0 of 10

    Text2Workflow is a multi-prompt LLM system with human feedback that generates JSON workflows from natural language, scoring 71.3% average semantic accuracy on the authors' 60-request Process2JSON dataset, versus 64.2%...

  5. A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI

    cs.CY 2025-05 conditional novelty 4.0 of 10

    A narrative review that aligns generative AI tools with formative assessment principles, provides classroom prompt examples, and identifies missing evaluation metrics for AI feedback.

  6. Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification

    cs.CL 2025-02 conditional novelty 4.0 of 10

    In a four-task benchmark, GPT-4o, Llama3.1-70B, and Gemma2-2B outperform traditional text simplification systems on most automatic metrics, and GPT-4o is preferred over human-written references in a small human study.

Pith tools