Pith. sign in

REVIEW 3 cited by

InstructIE: A Bilingual Instruction-based Information Extraction Dataset

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 2305.11527 v4 pith:U3GUAX22 submitted 2023-05-19 cs.CL cs.AIcs.IRcs.LG

Honghao Gui , Shuofei Qiao , Jintian Zhang , Hongbin Ye , Mengshu Sun , Lei Liang , Jeff Z. Pan , Huajun Chen
show 1 more author
Ningyu Zhang
This is my paper · ORCID
classification cs.CLcs.AIcs.IRcs.LG
keywords instructielanguagebilingualdatasetdatasetsextractioninformationinstruction-based
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large language models can perform well on general natural language tasks, but their effectiveness is still suboptimal for information extraction (IE). Recent works indicate that the main reason lies in the lack of extensive data on IE instructions. Note that the existing datasets on IE instructions not only have limited coverage but also involve high construction costs. To address this issue, we introduce InstructIE, a bilingual instruction-based IE dataset, which covers 12 diverse domains. We propose KG2Instruction, a framework specifically for the automatic generation of such datasets. Additionally, we manually annotate the test set. Experimental results demonstrate that large language models trained with InstructIE can not only obtain better IE capabilities but also enhance zero-shot performance compared with baselines.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text

    cs.AI 2026-05 conditional novelty 6.0 of 10

    A 24-dataset benchmark for inducing schema graphs from raw text, plus an auditable LLM-based pipeline that reports the highest scores on the benchmark's four schema-similarity metrics.

  2. Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A large synthetic instruction corpus with guidelines, preference rules, and format variants improves LLM performance on five NLU benchmarks by an average of 3.1%.

  3. Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA

    cs.CL 2025-09 conditional novelty 5.0 of 10

    Tea-MOELORA uses separate task and era gates over LoRA experts to jointly train relation and event extraction across classical and modern Chinese, improving F1 over joint LoRA and existing LoRA-MoE baselines on most datasets.

Pith tools