Venue: NAACL 2024

# GenDecider: Integrating "None of the Candidates" Judgments in

Zero-Shot Entity Linking Re-ranking

 Kang Zhou, Yuepei Li, Qing Wang, Qiao Qiao, Qi Li

Department of Computer Science, Iowa State University, Ames, Iowa, USA

{kangzhou, liyp0@95, qingwang, qqiao1, qli}@iastate.edu

###### Abstract

We introduce GenDecoder, a novel re-ranking approach for Zero-Shot Entity Linking (ZSEL), built on the Llama model. It innovatively detects scenarios where the correct entity is not among the retrieved candidates, a common oversight in existing re-ranking methods. By autoregressively generating outputs based on the context of the entity mention and the candidate entities, GenDecoder significantly enhances disambiguation, improving the accuracy and reliability of ZSEL systems, as demonstrated on the benchmark ZESHEL dataset. Our code is available at [https://github.com/KangISU/GenDecider](https://github.com/KangISU/GenDecider).

## 1 Introduction

Zero-Shot Entity Linking (ZSEL) Logeswaran et al. (2019), a crucial task in NLP, links entity mentions Zhou et al. (2022) in text to corresponding entities in a Knowledge Base (KB), when no labeled examples of those entities are available. The importance of this task stems from its ability to handle entities dynamically, particularly in evolving KBs where new entities frequently emerge.

The prevailing approach in ZSEL, exemplified by the BLINK method Wu et al. (2020), adopts a two-step process: initial retrieval of candidate entities followed by a re-ranking phase. While extensive research has improved the retrieval stage Ma et al. (2021); Agarwal et al. (2022); Sui et al. (2022); Sun et al. (2022); Wu et al. (2023), the re-ranking phase, which is critical for final decision-making, has received comparatively less attention.

Moreover, a significant oversight in existing re-ranking studies Wu et al. (2020); Tang et al. (2021); Xu et al. (2023) is the assumption that the correct entity is always among the retrieved candidates. This assumption, however, often does not hold in zero-shot settings, leading to the prevalence of what we call "None of the Candidates" (**NoC** for short) cases. When the correct entity is not among the retrieved candidates, opting for a NoC prediction is more beneficial than forcibly making a false positive prediction in real applications. Having NoC predictions can also offer feedback to the retrieval phase by highlighting the limitations of retrievers in zero-shot settings.

This paper introduces GenDecoder, a novel approach that integrates NoC judgments into the ZSEL re-ranking process. GenDecoder formulates the re-ranking task as a generative process using the recent Llama model Touvron et al. (2023). Given the context of an entity mention and the retrieved candidates, GenDecoder autoregressively generates an output that is either the ID of the correct entity candidate or a NoC judgment. This approach allows for direct interactions between the mention context and the candidates within the same input, facilitating more accurate disambiguation. Moreover, by supporting NoC judgments, GenDecoder enhances the reliability of ZSEL systems.

The contributions of this work are twofold. Firstly, it presents a novel re-ranking formulation that addresses a significant gap in existing research by effectively detecting NoC scenarios. Secondly, the proposed method demonstrates a comprehensive approach to disambiguation, improving both the accuracy and applicability of ZSEL systems.

## 2 Related Work

Entity Linking (EL) methods can be broadly classified into generation-based and retrieval-based. Generation-based methods, such as GENRE De Cao et al. (2020), directly generate entity titles but struggle with new entities in zero-shot settings Xu et al. (2023). In contrast, retrieval-based methods, more suitable for zero-shot settings, follow a two-step approach: candidate retrieval and re-ranking. Therefore, we follow the line of retrieval-based approaches for zero-shot settings and particularly enhance the re-ranking step



### ZSEL Re-ranking

The ZSEL task, initiated by Logeswaran et al. (2019), challenges EL systems' capability to link new, unseen entities using minimal information, typically just brief entity descriptions from KBs. Notable works in ZSEL re-ranking include BLINK Wu et al. (2020) which employs a Cross-Encoder for comprehensive analysis between mention contexts and entity descriptions. Bi-MPR Tang et al. (2021) utilizes a bidirectional multi-paragraph reading model for deeper semantic understanding, while ReS Xu et al. (2023) focuses on enhancing cross-entity comparisons. These approaches typically re-rank using similarity scores.

### Difference from NIL

The concept of NIL in EL refers to instances where an entity mention does not correspond to any entity in the entire KB Zhu et al. (2023). It signifies that the mention either refers to an entity not present in the KB or is not an entity. In contrast, NoC indicates that the correct entity does exist in the KB but was not included in the candidate set by the retrieval model. Therefore, while NIL concerns the absence of a corresponding entity in the KB, NoC deals with missing the correct entity in the retrieval process.

## 3 Methodology

### Task Definition

EL associates detected entity mentions in text with corresponding entities in KBs, typically through a two-step process: retrieval and re-ranking.

**Retrieval:** This phase aims to identify a set of candidate entities \({\cal C}(m)\) from the KB \({\cal E}\) for an entity mention \(m\) in a document \(d\).

**Re-ranking:** Following retrieval, this phase targets re-evaluating the candidate entities \({\cal C}(m)\) to accurately identify the correct entity \(e\).

ZSEL is characterized by that the training and test sets do not share entities, mirroring real-world scenarios where new, unseen entities frequently emerge. Formally, let \({\cal E}_{train}\) and \({\cal E}_{test}\) represent the training and test KBs, respectively, with \({\cal E}_{train}\cap{\cal E}_{test}=\emptyset\). Each entity \(e\) in either \({\cal E}_{train}\) or \({\cal E}_{test}\) is associated with a textual description \(Desc(e)\). Let \({\cal D}_{train}\) and \({\cal D}_{test}\) be the corresponding sets of training and test documents. The objective of ZSEL is to train a retrieval-reranking system using \({\cal D}_{train}\) and \({\cal E}_{train}\), and then apply it to \({\cal D}_{test}\) and \({\cal E}_{test}\).

### Integrating NoC into ZSEL Re-ranking

In this paper, we focus on enhancing the re-ranking phase. Traditional re-ranking methods typically assume that the correct entity is always present within the retrieved candidate set, which leads to a forced selection from this set. However, this assumption often does not hold in ZSEL scenarios, where the retrieval model (trained on \({\cal E}_{train}\)) is more likely to fail to include the correct entity in the candidate set from \({\cal E}_{test}\) compared to traditional EL. Consequently, this leads to a higher rate of false positives in the final linking predictions, thereby affecting the reliability of EL systems.

To tackle this challenge, we propose integrating the NoC option into re-ranking. We reformulate re-ranking as a generative task, employing a decoder-only architecture, which allows the model to directly reason over the mention context and candidate entities within the same input.

The input in our formulation includes a task-specific instruction \(Inst\), the context of the entity mention \(Ctxt(m)\), and the set of retrieved candidates \({\cal C}(m)\). The generated output is either the ID of the correct entity \(e\in{\cal C}(m)\), or a "None" designation when the correct entity is not among the candidates. This is formally represented as:

\[f:(Inst,\,Ctxt(m),\,{\cal C}(m))\to ID(e)\,or\,None,\]

where \((Inst,\,Ctxt(m),\,{\cal C}(m))\) collectively forms the prompt for our re-ranking process.

### GenDecider

In our empirical investigations, we discovered that recent open-sourced, decoder-only large language models (LLMs) struggled with our re-ranking formulation through In-Context Learning (ICL). This shortfall is likely attributable to their pre-training regimes, which may not heavily focus on disambiguation tasks. To overcome this limitation, we opted to fine-tune such an advanced LLM using Low-Rank Adaptation (LoRA) Hu et al. (2021). LoRA's adaptability allows us to retain the base model's capabilities while introducing a small, disambiguation-specialized adaptor, resulting in our new model, GenDecoder.

For training GenDecider, we collect entity mentions along with their top \(k\) candidates (i.e., \(|{\cal C}(m)|=k\)) given by the retrieval step as the training set. The choice of \(k\) is constrained by the base model's maximum context length. Note that this training set includes instances where the correct

entity \(e\) is not among \({\cal C}(m)\), leading to NoC scenarios. Instances serve to form the following prompt for GenDecider:

Entity Mention: \[m\] Entity Mention Context: \[Ctxt(m)\] Based on the above entity mention and its context, identify the ID of the candidate in the following to which the entity mention refers (if none of them, assign the ID as "None"): ID: \[ID(e_{0})\] Entity: \[e_{0}\] Entity Description: \[Desc(e_{0})\] (omit other \[k-1\] entity candidates) During training, we direct the model to generate a JSON object, for example, {"ID": "123"} or {"ID": "None"}, facilitating easy post-processing.

During inference, to improve the likelihood of including the correct entity in the candidate set, the number of candidates \(|{\cal C}(m)|\) can be larger than \(k\). Given GenDecoder may not process at once due to limited input length, we employ a block-wise approach. We split the candidates into \(\lceil|{\cal C}(m)|/k\rceil\) blocks for block-wise inference. Each block yields either a candidate prediction or a NoC prediction. We merge non-NoC predictions into a new set \({\cal C^{\prime}}(m)\). If \(|{\cal C^{\prime}}(m)|\) still exceeds \(k\), the process is repeated until the set meets the size criteria (\(\leq k\)). A final inference is then conducted on this set to get the ultimate prediction.

## 4 Experiments

### Datasets

We conduct experiments on the widely-used ZSEL dataset ZESHEL Logeswaran et al. (2019). Statistics of this dataset are presented in Table 1. All the mentions have their correct entities in the KBs, which allows us to experiment with NoC scenarios.

### Evaluation Metrics

Normalized accuracy is traditionally used in re-ranking evaluations, measuring the performance only on the **subset** of test instances for which the correct entity is within the retrieved candidates by the retrieval step. However, this metric falls short for our re-ranking methodology as it does not consider NoC cases, which are pivotal in our approach.

Instead, we adopt precision, recall, and the F1 score as our primary evaluation metrics on the **entire** test set. Precision can reflect if the NoC cases are accurately predicted, as correct identifications of NoC can reduce the rate of false positives. Recall measures whether the model accurately identifies the correct entity when it is within the candidate set. It's noteworthy that recall is essentially equivalent to normalized accuracy. The F1 score offers a balanced measure of the model's overall performance.

### Setups

We implement GenDecider on Vicuna-7B-v1.5, based on Llama 2 Touvron et al. (2023), with a limit of 4096 tokens. Both mention contexts and entity descriptions are limited to 256 tokens. For fine-tuning, we utilize the FastChat package, which supports LoRA. We set LoRA parameters to \(r=8\) and \(\alpha=16\), resulting in an adaptor with 4 million trainable parameters.

In our re-ranking experiments, we utilize BM25-retrieved candidates, consistent with prior studies (serve as baseline methods) Wu et al. (2020); Tang et al. (2021); Xu et al. (2023). Unlike baseline methods that use the top 64 candidates for both training and testing, GenDecider trains on the top 10 candidates (i.e., \(k=10\)) and aligns with the baselines by testing on the top 64 candidates. This setting ensures comparability with baselines while optimizing GenDecider's training efficiency.

The ZESHEL training set consists of 49,275 examples, including 30,614 examples where the correct entity is among the top 10 candidates and 18,661 examples where it is not, as identified by BM25. The training was conducted over 2 epochs

\begin{table}
\begin{tabular}{l c c} \hline Domain & Entities & Mentions \\ \hline  & **Training** & \\ \hline American Football & 31,929 & 3,898 \\ Doctor Who & 40,281 & 8,334 \\ Fallout & 16,992 & 3,286 \\ Final Fantasy & 14,044 & 6,041 \\ Military & 104,520 & 13,063 \\ Pro Wrestling & 10,133 & 1,392 \\ StarWars & 87,056 & 11,824 \\ World of Warcraft & 27,677 & 1,437 \\ \hline   **Validation** & & \\ \hline Coronation Street & 17,809 & 1,464 \\ Muppets & 21,344 & 2,028 \\ Ice Hockey & 28,684 & 2,233 \\ Elder Scrolls & 21,712 & 4,275 \\ \hline  & **Test** & \\ \hline Forgotten Realms & 15,603 & 1,200 \\ Lego & 10,076 & 1,199 \\ Star Trek & 34,430 & 4,227 \\ YuGiOh & 10,031 & 3,374 \\ \hline  & & \\ \end{tabular}
\end{table}
Table 1: Statistics of the ZESHEL dataset.

 

with a batch size of 1. Checkpoint selection was guided by loss convergence on a 2% held-out subset of our training data, differing from baseline methods that use the ZESHEL validation set. This selection is designed to better simulate a general zero-shot setting. All experiments were conducted on a single NVIDIA A100-SXM4-80GB GPU.

### Baseline Methods

For comparison, we include established re-ranking methods: BLINK Wu et al. (2020), Bi-MPR Tang et al. (2021), and ReS Xu et al. (2023), which do not incorporate NoC in their frameworks.

As NoC is a novel aspect in re-ranking studies, there are no existing baselines explicitly designed for it. For thorough evaluation, we propose two novel baseline methods to account for NoC, BLINK-Thresholding and ReS-Thresholding. Since BLINK and ReS use scoring for re-ranking, we introduce a thresholding mechanism to determine NoC. For an entity mention, if scores for all candidates are lower than the threshold, this instance is considered NoC. We conducted a grid search for thresholds (ranging from 0.1 to 0.9) on a subset of 500 training examples, aiming to maximize the F1 score. The best thresholds identified were 0.9 for BLINK and 0.1 for ReS. Bi-MPR was not adapted due to the unavailability of its code.

Additionally, we incorporate the base model Vicuna-7B-v1.5 through ICL (Vicuna-ICL) with a modified prompt from Section 3.3 by appending a suffix instruction: Only output the ID in this format {"ID": ""}, guiding its decoding. We append "(w/o none)" to Vicuna-ICL and GenDecoder to imply removing the instruction highlighted in blue from the prompt.

### Experimental Results

#### Prevalence of NoC Cases

Table 3 showcases the recall@64 performance for BM25 and Bi-Encoder Wu et al. (2020) on the ZESHEL test set. Bi-Encoder benefits from training on the ZESHEL training set and its ability to capture semantics, yielding an enhanced retrieval efficacy over BM25. However, both methods struggle in Star Trek and YuGiOh, which indicates the prevalence of NoC cases.

#### Performance on ZEHSEL Test Set

To be consistent with baselines, we use candidates retrieved by BM25 for the re-ranking evaluation. Table 2 offers a snapshot of the current state of ZSEL re-ranking methods.

In the group of not considering NoC, Vicuna-ICL (w/o none) underperforms, showing the base model's limitations in this disambiguation task. In contrast, GenDecider (w/o none) excels in this group, achieving the highest scores across datasets, underscoring the effectiveness of task-oriented fine-tuning and the advantages of larger language models in complex disambiguation tasks.

Introducing NoC predictions significantly improves precision, suggesting a reduction in false positives across most methods. This improved precision, coupled with robust recall rates, leads to notably higher F1 scores, demonstrating the importance of NoC in achieving a more balanced perfor

\begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline Method & Forgotten Realms & Lege & Star Trek & YuGiOh & Macro Avg. \\ \hline \multicolumn{5}{l}{**Not Considering NoC**} \\ \hline BLINK* & 72.33 / 86.80/ 78.91 & 62.05 / 76.39 / 68.48 & 51.36 / 77.95 / 61.92 & 41.05 / 67.46 / 51.04 & 56.70 / 77.15 / 65.36 \\ Bi-MPR* & 74.67 / 89.60/ 81.46 & 65.39 / 80.50 / 72.16 & 53.39 / 81.04 / 64.37 & 41.83 / 68.74 / 52.01 & 58.82 / 79.97 / 67.78 \\ ExtEnD* & 66.35 / 79.62 / 72.38 & 52.96 / 65.02 / 58.45 & 48.24 / 73.21 / 58.16 & 36.51 / 60.01 / 45.40 & 51.02 / 69.51 / 58.85 \\ Res* & 73.42 / 88.10 / 80.09 & 63.72 / 78.44 / 70.32 & 53.82 / 81.69 / 64.89 & 46.15 / 75.84 / 57.38 & 59.28 / 81.02 / 68.47 \\ Vicuna-ICL (w/o none) & 35.03 / 42.00 / 38.20 & 22.45 / 27.62 / 24.77 & 23.64 / 35.80 / 28.47 & 13.30 / 21.78 / 16.52 & 23.61 / 31.80 / 27.10 \\ GenDecider (w/o none) & 75.98 / **91.10** / 82.86 & 66.14 / **81.42** / 72.99 & 54.50 / **82.48** / 65.63 & 46.40 / **75.99** / 57.62 & 60.76 / **82.75** / 70.07 \\ \hline \multicolumn{5}{l}{**Considering NoC**} \\ \hline BLINK–Thresholding & **88.34** / 80.30 / 84.13 & **81.96** / 66.22 / 73.25 & 71.02 / 71.35 / 71.18 & 62.97 / 59.47 / 61.17 & **76.07** / 69.33 / 72.54 \\ Res-Thresholding & 85.95 / 77.10 / 81.28 & 78.20 / 61.50 / 68.85 & 73.97 / 70.09 / 71.98 & 62.21 / 68.87 / 65.37 & 75.12 / 69.39 / 72.12 \\ Vicuna-ICL & 35.10 / 37.80 / 36.40 & 22.09 / 24.54 / 23.25 & 22.46 / 30.92 / 26.02 & 14.09 / 21.44 / 17.01 & 23.44 / 28.67 / 25.79 \\ GenDecider & 86.26 / 86.00 / **86.13** & 79.06 / 72.90 / **75.85** & **74.75** / 79.61 / **77.10** & **63.60** / 73.11 / **68.03** & 75.92 / 77.91 / **76.90** \\ \hline \end{tabular}
\end{table}
Table 2: Performance (Precision/ Recall/F1) on ZESHEL test set. * means results reported in Xu et al. (2023). ExtEnd Barba et al. (2022), while not tailored for ZSEL, is included as a reference from conventional EL settings.

\begin{table}
\begin{tabular}{c|c|c} \hline Domain & BM25 & Bi-Encoder \\ \hline Forgotten Realms & 83.33 & 89.75 \\ Lego & 81.23 & 88.32 \\ Star Trek & 65.89 & 78.94 \\ YuGiOh & 60.85 & 65.65 \\ \hline \end{tabular}
\end{table}
Table 3: Retrieval performance (Recall@64) on ZESHEL test set.

 

mance. GenDecider shines in the NoC-inclusive group, topping F1 scores, maintaining strong recall, and achieving high precision, which affirms its efficacy and reliability in practical EL tasks when NoC is common. These insights confirm the crucial role of both NoC consideration and LLM fine-tuning for achieving accurate disambiguation.

#### 4.5.3 Category-Specific Performance

Mentions in the ZESHEL dataset Logeswaran et al. (2019) are categorized based on the token overlap with their corresponding entities:

* High Overlap (HO): The entity title is identical to the mention.
* Multiple Categories (MC): The entity title consists of the mention followed by a disambiguation phrase (e.g., for the mention 'Batman', the title is 'Batman (Lego)').
* Ambiguous Substring (AS): The mention is a substring of the entity title (e.g., the mention 'Agent' corresponds to the title 'The Agent').
* Low Overlap (LO): All other mentions that do not fit the above categories are considered low overlap.

These categories represent roughly 5%, 28%, 8%, and 59% of the dataset's mentions, respectively. Table 4 presents F1 scores on the ZESHEL test set over the four categories, showing that the impact of considering NoC may be differentiated by category.

For HO, incorporating NoC appears to detriment performance. This could indicate that when the mention and entity title match exactly, adding NoC consideration may introduce unnecessary complexity to the decision-making process. For AS, NoC inclusion shows a marginal improvement, suggesting that for subtle distinctions within the mentionity match, NoC can offer some disambiguation advantage. The benefits of considering NoC become more pronounced in MC and LO scenarios. The significant gains can be attributed to NoC effectively addressing instances where the retrieval step may not include the correct entity into the candidates, thereby reducing false positives and enhancing precision.

GenDecoder's top performance in MC and LO categories highlights its strength in the most challenging disambiguation scenarios, particularly where mentions have limited or no token overlap with entity titles.

#### 4.5.4 Robustness across Retrieval Methods

Figure 1 demonstrates the stability of re-ranking methods when applied to different retrieval strategies on the ZESHEL test set. Despite being trained with BM25-retrieved candidates, these methods exhibit consistent performance when assessed with Bi-Encoder-retrieved candidates Wu et al. (2020), showcasing their capacity to handle diverse candidate sets. GenDecider, in particular, retains high F1 scores across both retrieval methods, reinforcing its effectiveness amidst varying retrieval situations.

## 5 Conclusion

This paper presents GenDecoder, an innovative re-ranking approach for ZSEL that adeptly incorporates NoC judgments. Our extensive experiments on ZESHEL demonstrate that GenDecoder achieves superior performance in challenging disambiguation scenarios. The results underscore the importance of NoC consideration in improving the reliability in the re-ranking phase.

## 6 Acknowledgments

The work is supported by NIFA grant no. 2022-67015-36217 from the USDA National Institute of Food and Agriculture and the US National Science Foundation under grant NSF-CAREER 2237831.

\begin{table}
\begin{tabular}{c|c|c|c|c} \hline Method & HO & MC & AS & LO \\ \hline BLINK & 93.96 & 69.23 & 73.21 & 51.77 \\ Bi-MPR\({}^{*}\) & 92.50 & 75.23 & 70.85 & 52.04 \\ ReS\({}^{*}\) & **94.08** & 74.64 & 71.25 & 53.90 \\ GenDecoder (w/o none) & 91.06 & 75.71 & 78.30 & 55.31 \\ \hline BLINK-Thresholding & 90.84 & 74.44 & 74.05 & 62.20 \\ Res-Thresholding & 88.52 & 74.81 & 70.45 & 63.71 \\ GenDecoder & 90.69 & **78.59** & **79.42** & **68.95** \\ \hline \end{tabular}
\end{table}
Table 4: Category-specific performance (F1). * means results reported in Xu et al. (2023).

Figure 1: Robustness across retrieval methods.

 



## 7 Limitations

This study introduces GenDecider, a 7B-parameter model demonstrating state-of-the-art performance in zero-shot entity linking. However, there are limitations to consider.

**Computational Efficiency:** Due to its large size, GenDecoder is computationally intensive, which may not be feasible for systems requiring real-time or online processing. Its deployment in environments with limited computational resources could be challenging, potentially limiting its practicality for certain applications.

**Disambiguation Mechanism:** While GenDecoder shows promise, the underlying mechanisms of its disambiguation process may deserve further investigation. A deeper understanding of how GenDecoder differentiates between entities could lead to improvements in both model efficiency and interpretability.

Future work should focus on enhancing the model's computational efficiency and exploring the disambiguation mechanism in more detail, which may yield more lightweight and interpretable models without compromising performance.

## 8 Ethics Statement

We comply with the ACL Code of Ethics.





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