Pith. sign in

REVIEW 11 cited by

Large Language Models Meet NLP: A Survey

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 2405.12819 v2 pith:KBAUF4RQ submitted 2024-05-21 cs.CL cs.AI

Libo Qin , Qiguang Chen , Xiachong Feng , Yang Wu , Yongheng Zhang , Yinghui Li , Min Li , Wanxiang Che
show 1 more author
Philip S. Yu
This is my paper
classification cs.CLcs.AI
keywords llmslanguagetasksfirstlargemodelsparadigmpotential
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored. This study aims to address this gap by exploring the following questions: (1) How are LLMs currently applied to NLP tasks in the literature? (2) Have traditional NLP tasks already been solved with LLMs? (3) What is the future of the LLMs for NLP? To answer these questions, we take the first step to provide a comprehensive overview of LLMs in NLP. Specifically, we first introduce a unified taxonomy including (1) parameter-frozen paradigm and (2) parameter-tuning paradigm to offer a unified perspective for understanding the current progress of LLMs in NLP. Furthermore, we summarize the new frontiers and the corresponding challenges, aiming to inspire further groundbreaking advancements. We hope this work offers valuable insights into the potential and limitations of LLMs, while also serving as a practical guide for building effective LLMs in NLP.

Discussion (0). Sign in to comment.

Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 20 citations worldwide. Full citation record

  1. Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning

    cs.IR 2025-08 conditional novelty 6.0 of 10

    A schema-bounded agentic framework unifies graph construction and retrieval, reporting SOTA accuracy and up to 90.71% token-cost savings across six benchmarks, plus a new anonymized QA dataset.

  2. SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A controlled prompting protocol shows open-weight LLMs rely heavily on memorized Wikidata entities when writing SPARQL queries, with accuracy collapsing on a rarely used benchmark.

  3. Real-World Summarization: When Evaluation Reaches Its Limits

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Simple word-overlap and NLI metrics correlate best with human judgments of faithfulness for hotel highlights, while LLM judges are unreliable.

  4. MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios

    cs.SE 2025-06 conditional novelty 6.0 of 10

    MLDebugging: a new benchmark of 1,175 multi-library Python debugging tasks on which the best tested LLM, Llama-3.1-72B, passes only 58.7% of test cases.

  5. Quantifying Political Partisanship for Cross-Platform Analyses

    cs.SI 2026-07 reject novelty 5.0 of 10

    Partisanship of individual posts can be scored on a common embedding axis anchored by AllSides news-bias labels, yielding cross-platform scores that transfer from Bluesky/Truth Social to X.

  6. Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs

    cs.AI 2025-08 conditional novelty 5.0 of 10

    The study introduces TruthfulnessEval and reports that 4-bit quantization preserves simple true/false accuracy, but explicit 'lie' prompts make quantized and full-precision LLMs output falsehoods even when internal pr...

  7. ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Interleaving key video frames into step-by-step reasoning improves video question answering by 1.7 to 5.5 points over text-only chain-of-thought on a new self-built benchmark.

  8. Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Manager aggregates multi-layer unimodal representations and improves both two-tower VLMs (ManagerTower) and MLLMs (LLaVA-OV-Manager) on 24 downstream tasks.

  9. From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

    cs.AI 2026-06 conditional novelty 4.0 of 10

    Autonomous AI becomes dependable when tool use is embedded in persistent workspaces with reusable skills, shifting evaluation from answers to task closure.

  10. LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning

    cs.LG 2025-08 conditional novelty 4.0 of 10

    LLMDPD conditions an offline policy-diffusion model on LLM-embedded text task descriptions and a transformer-encoded trajectory prompt, reporting improved success on unseen Meta-World and D4RL tasks, though the evalua...

  11. Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements

    cs.SE 2025-06 conditional novelty 3.0 of 10

    Generating fixes with GPT-3.5 Turbo and GPT-4o, prompted with SonarQube findings and web-retrieved examples, removed most flagged bugs, vulnerabilities, and code smells from one codebase, with success judged solely by...

Pith tools