Pith. sign in

REVIEW 5 cited by

GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction

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 2310.03668 v5 pith:6AR6RUUZ submitted 2023-10-05 cs.CL

Oscar Sainz , Iker García-Ferrero , Rodrigo Agerri , Oier Lopez de Lacalle , German Rigau , Eneko Agirre This is my paper
classification cs.CL
keywords guidelinesableannotationgollieinformationmodelstasksunseen
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-specific models. Typically, IE tasks are characterized by complex annotation guidelines that describe the task and give examples to humans. Previous attempts to leverage such information have failed, even with the largest models, as they are not able to follow the guidelines out of the box. In this paper, we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines. Comprehensive evaluation empirically demonstrates that GoLLIE is able to generalize to and follow unseen guidelines, outperforming previous attempts at zero-shot information extraction. The ablation study shows that detailed guidelines are key for good results.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 17 citations worldwide. Full citation record

  1. LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Label-aware diagnostic reflection plus two-stage outcome GRPO improves same-backbone IE F1 over SFT, with larger gains under relation-extraction domain shift.

  2. PDB-Eval: An Evaluation of Large Multimodal Models for Description and Explanation of Personalized Driving Behavior

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Introduces PDB-Eval, a dual-view benchmark for fine-grained driver behavior description and explanation, and shows fine-tuning on it boosts performance on driving QA and downstream intention and recognition tasks.

  3. Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Syntactic similarity retrieval of demonstrations improves LLM-based automatic term extraction in cross-domain settings, but gains are modest and in-domain lexical retrieval is often competitive or better.

  4. KnowCoder-V2: Deep Knowledge Analysis

    cs.AI 2025-06 conditional novelty 5.0 of 10

    KnowCoder-V2 augments deep research with offline knowledge organization and code-based knowledge computation, reporting gains on information extraction, KBQA, and LLM-judged report generation.

  5. DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models

    cs.CL 2026-07 conditional novelty 4.0 of 10

    DPO after SFT plus constrained template-filling decoding improves LLM sequence-labeling accuracy and cuts inference time by reusing KV cache for non-label tokens.

Pith tools