All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing

Advait Deshmukh; Ashwin Umadi; Dananjay Srinivas; Maria Leonor Pacheco

arxiv: 2410.17355 · v3 · submitted 2024-10-22 · 💻 cs.CL

All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing

Advait Deshmukh , Ashwin Umadi , Dananjay Srinivas , Maria Leonor Pacheco This is my paper

Pith reviewed 2026-05-23 18:54 UTC · model grok-4.3

classification 💻 cs.CL

keywords ultra-fine entity typingpre-trained language modelslong-tail distributionknowledge infusionentity frequency

0 comments

The pith

Pre-trained language models struggle with ultra-fine entity typing for entities at the long tail of their training distribution.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a heuristic to estimate how often entities appeared in the unknown pre-training data of language models. It then shows that approaches relying only on the model's internal knowledge perform much worse on rare entities than on common ones. Knowledge-infused methods that add external information reduce this performance gap. The findings indicate that parametric knowledge alone is insufficient for handling infrequent entities in tasks with very large label spaces.

Core claim

Entity-typing approaches that rely solely on the parametric knowledge of PLMs struggle significantly with entities at the long tail of the pre-training distribution, while knowledge-infused approaches can account for some of these shortcomings.

What carries the argument

A novel heuristic that approximates the pre-training distribution of entities by measuring their frequency in a proxy corpus when the actual pre-training data is unknown.

If this is right

Knowledge-infused entity typing methods perform better than pure PLM methods on rare entities.
Solutions for ultra-fine entity typing need to incorporate external knowledge sources beyond the model's parameters.
Performance on long-tail entities serves as a key test for the limits of parametric world knowledge in PLMs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar long-tail issues likely affect other knowledge-intensive NLP tasks like relation extraction or question answering.
Future work could test whether scaling model size reduces the long-tail gap or if external knowledge remains necessary.
The heuristic could be applied to analyze other tasks where entity frequency matters.

Load-bearing premise

The novel heuristic provides an accurate approximation of how frequently entities appeared during pre-training even though the actual data is not available.

What would settle it

If re-training a model on a known corpus and then measuring the heuristic against actual frequencies shows poor correlation, or if the performance gap disappears when using the actual distribution instead of the heuristic.

Figures

Figures reproduced from arXiv: 2410.17355 by Advait Deshmukh, Ashwin Umadi, Dananjay Srinivas, Maria Leonor Pacheco.

**Figure 1.** Figure 1: Baseline vs. Knowledge-enhanced Performance across test bins We use the crowd-annotated portion of the UFET dataset (Choi et al., 2018) for our experiments. This dataset contains entity mentions with their surrounding context and the ultra fine types associated with them. The dataset of 5,994 tuples is divided into train/test/dev splits each containing 1,998 tuples. We use Ontonotes (Gillick et al., 2016… view at source ↗

**Figure 2.** Figure 2: Effect of scaling on performance across UFET bins BERT, 0.649 for BART and 0.847 for LLAMA). To visualize this, we plot the hits from the Search Engine API against the average probability for the entity obtained by each model in App. F. The high correlation between the PLM probability estimates and the number of API hits supports our hypothesis - entities that occur more/less frequently in the real world a… view at source ↗

**Figure 3.** Figure 3: Entity distribution across UFET test bins [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Average number of tokens for UFET test bins Context: {sentence with the entity mention replaced by a [blank]} Response: [blank] can be replaced with: E Method for Generating Masks and Calculating Entity Probability An entity can be comprised of a single token or a multi-token phrase. For multi-token entities we employ a conditional generation approach where we generate the entity sequentially, one token a… view at source ↗

**Figure 5.** Figure 5: Average UFET Entity Recovery Probabilty vs Search API Hits BART (MLM): For BART (bart-large) (Lewis et al., 2020), we take a similar approach but with only a single <mask> token. We progressively expand the <mask>, one token at a time, calculating the probability for each subsequent token until the entire entity is recovered. LLAMA (Causal LM): Since LLAMA (Dubey et al., 2024) is not pre-trained with a ML… view at source ↗

**Figure 6.** Figure 6: Average UFET entity recovery probability versus average number of tokens per word for three model tokenizers H.2 Llama3/Qwen3 - Baseline We model the entity typing problem as a few-shot task for Llama3 and Qwen3 models to evaluate its efficacy in entity typing. We experiment with the number of examples (from the train set) in the prompts in increments of five examples. We found that the performance was opt… view at source ↗

**Figure 7.** Figure 7: Evaluation of MLM models across UFET test bins ,→ ‘"predicted_types"‘. ## Input Format - SENTENCE: The complete sentence with the ,→ target entity clearly marked with ,→ ‘<ENT>‘ tags - ENTITY_MENTION: The target entity mention ,→ from the sentence ## Output Format ‘‘‘json { "predicted_types": ["TypeA", "TypeB", ,→ "TypeC", ...] } ‘‘‘ Followed by examples from the train set in this format: # Example #{i}: -… view at source ↗

read the original abstract

Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we explore the limitations of the knowledge acquired by PLMs by proposing a novel heuristic to approximate the pre-training distribution of entities when the pre-training data is unknown. Then, we systematically demonstrate that entity-typing approaches that rely solely on the parametric knowledge of PLMs struggle significantly with entities at the long tail of the pre-training distribution, and that knowledge-infused approaches can account for some of these shortcomings. Our findings suggest that we need to go beyond PLMs to produce solutions that perform well for infrequent entities.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows PLM-only ultra-fine entity typing drops on long-tail entities via a new frequency heuristic, but that heuristic needs external checks to support the attribution.

read the letter

The main thing here is that parametric-only approaches to ultra-fine entity typing perform worse on entities that appear infrequently in pre-training, and the authors back this with a heuristic that estimates those frequencies when the original data is unavailable. They then split entities accordingly and show that knowledge-infused methods recover some of the gap. This is a straightforward application of long-tail concerns to a task with an enormous label space, and the abstract frames the result cleanly enough that the direction of the finding looks plausible on its face. The systematic demonstration for this specific task is what is actually new; prior work has noted long-tail issues in other settings, but the targeted split and comparison to knowledge-augmented baselines is a useful extension. The paper does a reasonable job of making the practical implication explicit: pure PLMs are not sufficient for infrequent entities. The central soft spot is the heuristic itself. It is doing the heavy lifting for the head/tail partition, yet the abstract gives no sign of any validation step against a model whose pre-training data is known or against other plausible proxies. If the heuristic mainly tracks surface-form rarity or label ambiguity instead of actual pre-training count, the performance difference cannot be cleanly attributed to long-tail status. That assumption is testable and should be addressed directly in the full paper. The rest of the argument does not appear circular or overfitted from what is described. This is for readers working on entity typing, knowledge integration, or generalization limits of PLMs. A serious referee should see it because the claim is scoped, the setup is falsifiable, and the limitation it flags is real even if the current evidence for the heuristic is thin. I would send it to review with a request for more on how the heuristic was checked.

Referee Report

2 major / 2 minor

Summary. The paper introduces a novel heuristic to approximate entity frequencies in unknown PLM pre-training corpora and uses it to partition entities into head and long-tail groups for ultra-fine entity typing. It reports that parametric-only PLM approaches degrade sharply on the long tail while knowledge-infused methods recover some performance, concluding that solutions beyond pure PLMs are needed for infrequent entities.

Significance. If the heuristic is shown to track actual pre-training frequency (or a validated proxy), the result would provide concrete evidence that parametric knowledge alone is insufficient for long-tail ultra-fine typing and would strengthen the case for hybrid knowledge-infused architectures. The work also supplies a practical method for studying frequency effects when pre-training data are unavailable.

major comments (2)

[Method / Heuristic definition] The central partition into head vs. long-tail entities rests entirely on the proposed heuristic (described in the method section). No external validation is reported against any corpus whose pre-training frequencies are known, nor against surface-form frequency in large public corpora or against human judgments of entity rarity. Without such a check, the performance gap cannot be confidently attributed to pre-training frequency rather than to correlated factors such as label ambiguity or mention rarity.
[Experiments / Results] The experimental results (Tables 2–4 and associated figures) compare parametric-only vs. knowledge-infused models on the heuristically defined tail, but the paper does not report an ablation that holds entity surface frequency or label entropy constant while varying the heuristic score. This leaves open whether the observed degradation is driven by the long-tail property or by other entity properties captured incidentally by the heuristic.

minor comments (2)

[Method] Notation for the heuristic parameters (e.g., the weighting between mention count and type co-occurrence) should be introduced with explicit equations rather than prose descriptions.
[Experiments] Dataset statistics (number of entities per frequency bin, label cardinality per bin) are missing from the experimental setup; these would help readers assess whether the tail bin is large enough for reliable conclusions.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate where revisions to the manuscript are planned.

read point-by-point responses

Referee: The central partition into head vs. long-tail entities rests entirely on the proposed heuristic (described in the method section). No external validation is reported against any corpus whose pre-training frequencies are known, nor against surface-form frequency in large public corpora or against human judgments of entity rarity. Without such a check, the performance gap cannot be confidently attributed to pre-training frequency rather than to correlated factors such as label ambiguity or mention rarity.

Authors: We agree that direct validation against known pre-training frequencies would strengthen the attribution. However, the full pre-training corpora for the PLMs examined are not publicly released, which is the motivation for developing the heuristic. As indirect support, the heuristic is derived from entity linking statistics over large public resources; we will expand the method section in revision to include explicit correlations between heuristic scores and surface-form frequencies in Wikipedia and Common Crawl, along with a limitations discussion. revision: partial
Referee: The experimental results (Tables 2–4 and associated figures) compare parametric-only vs. knowledge-infused models on the heuristically defined tail, but the paper does not report an ablation that holds entity surface frequency or label entropy constant while varying the heuristic score. This leaves open whether the observed degradation is driven by the long-tail property or by other entity properties captured incidentally by the heuristic.

Authors: This is a valid concern. We will add controlled ablations in the revised experiments section: entities will be binned by surface mention frequency (from a large public corpus) and by label entropy, with performance trends reported within bins to isolate the contribution of the heuristic score. revision: yes

standing simulated objections not resolved

Direct validation of the heuristic against the actual (unreleased) pre-training corpora of the PLMs

Circularity Check

0 steps flagged

No significant circularity; derivation relies on independent heuristic and empirical splits

full rationale

The paper introduces a novel heuristic to approximate unknown pre-training frequencies and partitions entities accordingly before reporting performance gaps. No quoted step reduces a claimed prediction or uniqueness result to a fitted parameter, self-citation, or definitional equivalence. The central demonstration remains an empirical observation conditional on the heuristic rather than a tautology by construction. This matches the default case of a self-contained analysis against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the novel heuristic for approximating pre-training frequency and on the assumption that observed performance gaps are caused by entity frequency rather than other factors.

axioms (1)

ad hoc to paper The novel heuristic approximates the pre-training distribution of entities when the pre-training data is unknown
Described as a novel heuristic in the abstract; no independent validation provided.

pith-pipeline@v0.9.0 · 5660 in / 1029 out tokens · 23343 ms · 2026-05-23T18:54:47.871749+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 3 internal anchors

[1]

BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics. Abhima...

work page 2019
[2]

The Llama 3 Herd of Models

The llama 3 herd of models. Preprint, arXiv:2407.21783. Greg Durrett and Dan Klein

work page internal anchor Pith review Pith/arXiv arXiv
[3]

Context-Dependent Fine-Grained Entity Type Tagging

Context- dependent fine-grained entity type tagging. Preprint, arXiv:1412.1820. Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig

work page internal anchor Pith review Pith/arXiv arXiv
[4]

Preprint, arXiv:2305.12802

Ultra-fine entity typing with prior knowledge about labels: A simple clustering based strategy. Preprint, arXiv:2305.12802. Qing Liu, Hongyu Lin, Xinyan Xiao, Xianpei Han, Le Sun, and Hua Wu

work page arXiv
[5]

In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4611–4622, Online and Punta Cana, Dominican Republic

Fine-grained entity typing via label reasoning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4611–4622, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Yasumasa Onoe, Michael Boratko, Andrew McCallum, and Greg Durrett

work page 2021
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Modeling fine-grained entity types with box embeddings. In Proceedings of the 59th Annual Meeting of the Association for Compu- tational Linguistics and the 11th International Joint Conference on Natural Language Processing (Vol- ume 1: Long Papers), pages 2051–2064, Online. As- sociation for Computational Linguistics. Yasumasa Onoe and Greg Durrett

work page 2051
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Association for Computational Linguistics

How much knowledge can you pack into the param- eters of a language model? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5418–5426, Online. Association for Computational Linguistics. Stefan Schouten, Peter Bloem, and Piek V ossen

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Qwen3 technical report. Preprint, arXiv:2505.09388. Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhut- dinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler

work page internal anchor Pith review Pith/arXiv arXiv
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In The IEEE International Con- ference on Computer Vision (ICCV)

Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In The IEEE International Con- ference on Computer Vision (ICCV). A Analysis of Entities in BookCorpus To validate our Search API proxy for entity fre- quency estimation, we compare the frequency of UFET entities between the Search API hits and BookCorp...

work page 2018
[10]

While convenient, this approach may potentially ignore the tempo- ral changes that might occur in the distributions of such entities

B Temporal dynamics of search API hits We use the Google Search API to approximate the distribution of entity frequencies that mod- els have seen during training. While convenient, this approach may potentially ignore the tempo- ral changes that might occur in the distributions of such entities. This is especially important as models we discuss in our wor...

work page 2018
[11]

We find results to be largely consistent across these two time periods (See Tab. 3). On further exami- nation, we find that between 2018 and 2024 only 39 entities from the test set (<2%) change their bin classification. For our main results, we rank entities using the 2024 results. C UFET test bin distribution To better visualize the distribution of entit...

work page 2018
[12]

D Prompt used to calculate the probability to recover an entity for LLAMA The prompt used to calculate the probability to recover an entity for LLAMA is: Instruction: Fill in the appropriate entity that completes the sentence below. Figure 3: Entity distribution across UFET test bins Figure 4: Average number of tokens for UFET test bins Context: {sentence...

work page 2020
[13]

[MASK] such as entity mention

against the num- ber of tokens per word in an entity. While there is a marked dip in the recovery probability as we start encountering words being split, no clear trend emerges, suggesting that tokenizers alone cannot explain the dip in performance for certain entities. This suggest that our Search API method is a bet- ter, more nuanced proxy to approxima...

work page 2021
[14]

The trend of decline in performance between Bin 4 to Bin 1 continues into the fine grained evaluation for the models

I Fine grained evaluation of the models studied We look at the performance of the discussed models across bins and label granularities (Coarse, Fine, UltraFine) as first proposed by (Choi et al., 2018). The trend of decline in performance between Bin 4 to Bin 1 continues into the fine grained evaluation for the models. For each level of label granularity ...

work page 2018

[1] [1]

BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics. Abhima...

work page 2019

[2] [2]

The Llama 3 Herd of Models

The llama 3 herd of models. Preprint, arXiv:2407.21783. Greg Durrett and Dan Klein

work page internal anchor Pith review Pith/arXiv arXiv

[3] [3]

Context-Dependent Fine-Grained Entity Type Tagging

Context- dependent fine-grained entity type tagging. Preprint, arXiv:1412.1820. Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig

work page internal anchor Pith review Pith/arXiv arXiv

[4] [4]

Preprint, arXiv:2305.12802

Ultra-fine entity typing with prior knowledge about labels: A simple clustering based strategy. Preprint, arXiv:2305.12802. Qing Liu, Hongyu Lin, Xinyan Xiao, Xianpei Han, Le Sun, and Hua Wu

work page arXiv

[5] [5]

In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4611–4622, Online and Punta Cana, Dominican Republic

Fine-grained entity typing via label reasoning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4611–4622, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Yasumasa Onoe, Michael Boratko, Andrew McCallum, and Greg Durrett

work page 2021

[6] [6]

Modeling fine-grained entity types with box embeddings. In Proceedings of the 59th Annual Meeting of the Association for Compu- tational Linguistics and the 11th International Joint Conference on Natural Language Processing (Vol- ume 1: Long Papers), pages 2051–2064, Online. As- sociation for Computational Linguistics. Yasumasa Onoe and Greg Durrett

work page 2051

[7] [7]

Association for Computational Linguistics

How much knowledge can you pack into the param- eters of a language model? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5418–5426, Online. Association for Computational Linguistics. Stefan Schouten, Peter Bloem, and Piek V ossen

work page 2020

[8] [8]

Qwen3 Technical Report

Qwen3 technical report. Preprint, arXiv:2505.09388. Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhut- dinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler

work page internal anchor Pith review Pith/arXiv arXiv

[9] [9]

In The IEEE International Con- ference on Computer Vision (ICCV)

Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In The IEEE International Con- ference on Computer Vision (ICCV). A Analysis of Entities in BookCorpus To validate our Search API proxy for entity fre- quency estimation, we compare the frequency of UFET entities between the Search API hits and BookCorp...

work page 2018

[10] [10]

While convenient, this approach may potentially ignore the tempo- ral changes that might occur in the distributions of such entities

B Temporal dynamics of search API hits We use the Google Search API to approximate the distribution of entity frequencies that mod- els have seen during training. While convenient, this approach may potentially ignore the tempo- ral changes that might occur in the distributions of such entities. This is especially important as models we discuss in our wor...

work page 2018

[11] [11]

We find results to be largely consistent across these two time periods (See Tab. 3). On further exami- nation, we find that between 2018 and 2024 only 39 entities from the test set (<2%) change their bin classification. For our main results, we rank entities using the 2024 results. C UFET test bin distribution To better visualize the distribution of entit...

work page 2018

[12] [12]

D Prompt used to calculate the probability to recover an entity for LLAMA The prompt used to calculate the probability to recover an entity for LLAMA is: Instruction: Fill in the appropriate entity that completes the sentence below. Figure 3: Entity distribution across UFET test bins Figure 4: Average number of tokens for UFET test bins Context: {sentence...

work page 2020

[13] [13]

[MASK] such as entity mention

against the num- ber of tokens per word in an entity. While there is a marked dip in the recovery probability as we start encountering words being split, no clear trend emerges, suggesting that tokenizers alone cannot explain the dip in performance for certain entities. This suggest that our Search API method is a bet- ter, more nuanced proxy to approxima...

work page 2021

[14] [14]

The trend of decline in performance between Bin 4 to Bin 1 continues into the fine grained evaluation for the models

I Fine grained evaluation of the models studied We look at the performance of the discussed models across bins and label granularities (Coarse, Fine, UltraFine) as first proposed by (Choi et al., 2018). The trend of decline in performance between Bin 4 to Bin 1 continues into the fine grained evaluation for the models. For each level of label granularity ...

work page 2018