Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remains challenging. Supporting user-specified types alone does not resolve this problem. We present CalOn, a unified 0.6B-parameter encoder for entity, relation, and event extraction across datasets and schemas. The model jointly encodes text and type definitions, then directly identifies entity spans, relations between spans, and event triggers and arguments. Its extraction decisions consider the requested types together, using their combined evidence to identify relevant candidates before assigning a type. This gives all three tasks a common probabilistic formulation while keeping every extracted mention anchored to the input text. We train on diverse sources, and supplement supervised learning with a reward that encourages correct extractions and penalises incorrect ones. Across 40 benchmarks, CalOn outperforms the evaluated baselines on most benchmarks. It leads all reported relation extraction benchmarks and achieves the highest average F1 for event triggers and arguments with gold triggers. The same model also performs strongly on aspect sentiment triplet extraction and pronoun resolution through its span-pair formulation. A correction for the probability bias introduced during training improves confidence estimates for entities, relations, and event arguments after calibration, with little change in F1.
Figures & tables
Figure 1: Overview of CalOn : (a) schema-text encoding with type-candidate interaction and bilinear scoring over the queried types and a null class, and (b) the RLCD reward on sampled typed decisions added to the supervised loss.
Figure 2: Ablation results for CalOn . Each row changes one model component. Bars show average micro F1 (%) with type descriptions over 15 NER benchmarks, 18 RE benchmarks with gold entities, and 7 EE benchmarks with gold triggers. Stars ( ⋆ ) show results with type names alone on their own scales. The bottom row shows the strongest baseline for each task, averaged over the same benchmarks. InstructUIE’s training data include some of the event benchmarks.
Appendix figures & tables9 assets
Supplementary material from the paper’s appendix.
Appendix
Model
Training mentions of the name
ρ
Rare
Middle
Frequent
CalOn
58.8
54.8
61.3
0.04
GLiNER2
50.6
46.7
52.0
0.01
GLiFormer
50.0
50.6
55.2
0.09
GNER
36.1
42.7
48.7
0.21
InstructUIE
38.7
39.5
44.2
0.10
Appendix
Table 5: Macro F1 (%) over the 150 entity types in the 15 NER benchmarks, counting types separately for each benchmark. Types are grouped into thirds by their mention counts in CalOn ’s training data. Rare types have at most 13,400 mentions, and frequent types have more than 77,924. ρ is the Spearman correlation between these counts and per-type F1. Evaluation settings follow Table 1 .
F1 (%)
ECE
Dataset
Fitted
+Corr.
+Corr.+ T
Fitted
+Corr.
+Corr.+ T
Entities
Validation (training sources)
68.4
68.5
68.4
0.25
0.16
0.12
CrossNER AI
66.1
66.0
65.8
0.26
0.21
0.17
CrossNER Literature
63.5
63.3
63.1
0.27
0.22
0.18
CrossNER Music
72.0
71.7
71.4
0.18
0.15
0.10
Appendix
Table 9: F1 and ECE for CalOn . Fitted uses the original calibration parameters without null-class correction. +Corr. applies the correction and refits thresholds with T=1 . +Corr.+ T additionally refits temperature. Parameters are selected on validation data from the training sources, using REBEL for relations and event arguments, and remain fixed on evaluation datasets. Relations use gold entities, and event arguments use gold triggers. ECE uses 15 equal-width bins over returned predictions and the probability of the predicted type or role. Lowest ECE values are bold .
Figure 3: Average micro F1 as training data increase. Solid lines use type descriptions, dashed lines use type names alone, and dotted lines show GLiNER2 with descriptions. Averages cover the 15 NER, 18 RE with gold entities, and 7 EE with gold triggers benchmarks.
Dataset
Task
Domain and genre
# Types
# Examples
CrossNER AI Liu et al. (2021)
Entities
AI, encyclopedia
14
431
CrossNER Literature Liu et al. (2021)
Entities
Literature, encyclopedia
12
416
CrossNER Music Liu et al. (2021)
Entities
Music, encyclopedia
13
465
CrossNER Politics Liu et al. (2021)
Entities
Politics, encyclopedia
9
651
CrossNER Science Liu et al. (2021)
Entities
Science, encyclopedia
17
543
MIT Movie Liu et al. (2013)
Entities
Film, search queries
12
1,953
Appendix
Table 11: The 40 evaluation benchmarks with their tasks, domains, genres, type counts, and numbers of converted evaluation inputs. Types are entity, relation, or event types according to the task. Inputs may be sentences, passages, or document chunks. All scores use micro F1.
Dataset
Task
Domain and genre
Annotation
# Types
# Examples
Few-NERD Ding et al. (2021)
Entities
General, encyclopedia
Human
74
125,839
MASSIVE FitzGerald et al. (2023)
Entities (slots)
Personal assistant, voice commands
Human
55
11,062
FiNERWeb Golde et al. (2026)
Entities
General, web pages
LLM (released)
3,895
16,798
Pile-NER Zhou et al. (2024)
Entities
General, mixed text
LLM (released)
268,736
73,395
REBEL Huguet Cabot and Navigli (2021)
Entities and relations
General, encyclopedia
LLM (ours)
45,074
399,983
MAVEN-Arg Wang et al. (2024)
Events
General, encyclopedia
Human
162
12,889
Appendix
Table 12: Training sources using real texts. Human denotes the original human annotations, LLM (released) denotes released LLM annotations, and LLM (ours) denotes annotations produced by our pipeline. Type counts include names with at least one training annotation, counting relation types for REBEL and event types for event sources. Example counts refer to converted training instances, including trigger and argument instances derived from event passages. Synthetic sources are described in Table 13 and Figure 5 .
Genres
email; text message conversation; internal business report; meeting notes; social media post; product review; short news brief; customer support ticket; invoice or order confirmation; job posting; contract clause; clinical note; research paper abstract; recipe; travel itinerary; forum discussion post; press release; resume summary; encyclopedia-style paragraph; podcast transcript excerpt; spoken request to a voice assistant; search query log; legal contract summary; government notice; patent abstract; wiki talk page comment; product specification sheet; hospital discharge summary; sports match report; obituary; museum exhibit label; travel guide paragraph; restaurant menu description; movie synopsis; tweet thread; answer on a question-and-answer site; tutorial step; software changelog entry; science news article; court judgment excerpt
Domains
finance and banking; healthcare; pharmaceuticals; law and courts; politics and elections; government and public policy; military and defense; sports; music; film and television; literature and publishing; visual arts; video games; fashion; food and restaurants; travel and tourism; hotels and hospitality; real estate; construction and architecture; automotive; aviation; shipping and logistics; retail and e-commerce; consumer electronics; telecommunications; software engineering; cybersecurity; artificial intelligence research; data science; physics; chemistry; biology and genetics; astronomy and space; earth science and climate; agriculture; energy and utilities; oil and gas; mining; manufacturing; human resources; education; academia and universities; religion; history; archaeology; philosophy; linguistics; journalism and media; advertising and marketing; insurance; cryptocurrency and blockchain; nonprofit and charity; environmental conservation; veterinary and animals; sports betting and gaming; social work; public transport; weddings and events; fitness and wellness; parenting and family; ophthalmology; dentistry; mental health; nutrition; tennis; association football; basketball; baseball; cricket; motorsport; cycling; the Olympic Games; chess; board games; anime and manga; theatre; dance; photography; sculpture; museums; classical antiquity; medieval history; military history; genealogy; urban planning; maritime industry; railways; space industry; robotics; semiconductors; cloud computing; open-source software; mobile apps; social networks; e-sports; tax law; intellectual property; immigration; criminal justice; human rights; international relations; diplomacy; local government; labor unions; supply chains; wine and beverages; coffee and tea; gardening; pets; zoology; botany; marine biology; ecology; geology; meteorology; oceanography; epidemiology; neuroscience; materials science; mathematics
Appendix
Table 13: Genres and domains used for synthetic generation. Each request combines one genre with one domain and asks the generator to produce a text and an extraction schema.
Figure 4: Training type frequencies. For each source, types are ranked by mention count. Both axes use logarithmic scales. Longer curves indicate more type names, and flatter curves indicate a more even distribution of mentions. The synthetic curve pools all synthetic sources, while the other curves show individual datasets. Counts use the complete training files before sampling.
Figure 5: Generation prompts for entities, relations, and events. The genre and domain values come from Table 13 . Requested text lengths are 30, 60, 120, 200, or 300 words. Types listed as absent provide explicit negative supervision after annotation checking.
Figure 6: UMAP ( McInnes et al., 2018 ) projection of sentence embeddings sampled from synthetic training sources, real-text training sources, and evaluation datasets.
Joint named entity recognition (NER) and relation extraction (RE) is a fundamental task in natural language processing for constructing knowledge graphs from unstructured text. While recent approaches treat NER and RE as separate tasks requiring distinct models, we introduce GLiNER-Relex, a unified architecture that extends the GLiNER framework to perform both entity recognition and relation extraction in a single model. Our approach leverages a shared bidirectional transformer encoder to jointly represent text, entity type labels, and relation type labels, enabling zero-shot extraction of arbitrary entity and relation types specified at inference time. GLiNER-Relex constructs entity pair representations from recognized spans and scores them against relation type embeddings using a dedicated relation scoring module. We evaluate our model on four standard relation extraction benchmarks: CoNLL04, DocRED, FewRel, and CrossRE, and demonstrate competitive performance against both specialized relation extraction models and large language models, while maintaining the computational efficiency characteristic of the GLiNER family. The model is released as an open-source Python package with a simple inference API that allows users to specify arbitrary entity and relation type labels at inference time and obtain both entities and relation triplets in a single call. All models and code are publicly available.
End-to-end event extraction remains challenging for large language models as it requires simultaneous identification of event triggers, classification of event types, and extraction of schema-grounded argument spans. We present EAGER, a reinforcement learning framework for generative event extraction that combines fine-grained verifiable rewards with Schema-Contrastive Advantage Estimation to alleviate advantage collapse under sparse binary rewards. Our reward design explicitly targets structural validity, extraction accuracy, groundedness, coverage, over-generation, and span precision. Experiments across seven benchmark datasets show that EAGER consistently outperforms prompting, supervised fine-tuning, and prior reinforcement learning baselines, achieving a substantial improvement over the strongest prior method. Results demonstrate that task-aligned verifiable rewards and contrastive advantage estimation substantially improve structured extraction.
Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative sequence-to-sequence framework that performs event extraction subtasks jointly and supports both pipeline and end-to-end configurations. We fine-tune pretrained language models on multiple event datasets across diverse domains, enabling a single model to retain domain-specific semantics while generalizing over large and evolving label spaces. We demonstrate these capabilities through a web-based application tailored for researchers and practitioners. The platform supports document upload, schema-aware event extraction, visualization of triggers and arguments, and comparison of different extraction configurations across domains.