Papers by Nicola Cancedda
Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation (2026.acl-industry)
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Bhaktipriya Radharapu, Eshika Saxena, Kenneth Li, Chenxi Whitehouse, Adina Williams, Nicola Cancedda
| Challenge: | Existing methods for obtaining well-calibrated uncertainty estimates are poorly calibrated or computationally expensive. |
| Approach: | They propose a linear probe that provides calibrated uncertainty estimates from reasoning judges’ hidden states, requiring no additional model training. |
| Outcome: | The proposed method achieves superior calibration compared to existing methods with x computational savings, generalizes robustly to unseen evaluation domains, and delivers higher accuracy on high-confidence predictions. |
Spectral Filters, Dark Signals, and Attention Sinks (2024.acl-long)
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| Challenge: | Recent work assigns a central role to the model's residual stream as the shared communication channel between model components. |
| Approach: | They propose a quantitative extension of the logit lens approach by partitioning the embedding and unembedding matrices into bands and spectral filters on intermediate representations. |
| Outcome: | The proposed model can suppress the tail end of the embedding spectrum, but it is not able to suppress large parts of the spectrum. |
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing (2022.emnlp-main)
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| Challenge: | Existing work on Entity Linking assumes that the knowledge base is complete and all mentions can be linked. |
| Approach: | They propose a temporally segmented Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities have to be integrated into existing entity linking systems. |
| Outcome: | The proposed system detects, clusters, and indexes mentions of unknown entities in context. |
Multilingual Autoregressive Entity Linking (2022.tacl-1)
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Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni
| Challenge: | mGENRE is a sequence-to-sequence system for multilingual entity linking . mGenRE is used to solve language-specific mentions to a multilingual Knowledge Base . |
| Approach: | They propose a sequence-to-sequence system for multilingual entity linking . they match language-specific mentions against a multilingual Knowledge Base (KB) mGENRE is a sequential system that predicts the name of the target entity token-by-token . |
| Outcome: | The proposed system improves on three popular MEL benchmarks and shows improvements in accuracy. |
HalluLens: LLM Hallucination Benchmark (2025.acl-long)
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Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung
| Challenge: | Large language models (LLMs) generate responses that deviate from user input or training data, a phenomenon known as "hallucination" . |
| Approach: | They propose a hallucination benchmark HalluLens that includes both extrinsic and intrinsic evaluation tasks to distinguish between extrindic and intrinsic hallucines. |
| Outcome: | The proposed framework disentangles LLM hallucination from "factuality" and distinguishes between extrinsic and intrinsic hallucines to promote consistency and facilitate research. |
Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings (2023.emnlp-main)
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Mattia Atzeni, Mikhail Plekhanov, Frederic Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda
| Challenge: | Entity linking methods based on dense retrieval are often not efficient in large-scale applications as they are sensitive to the structure of the embedding space. |
| Approach: | They propose a method to infuse structural information into the space of entity representations by using prior knowledge of entity types. |
| Outcome: | The proposed method outperforms other type-aware approaches and matches generative models with 18 times more parameters. |
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations (2025.emnlp-main)
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Ziwei Ji, Lei Yu, Yeskendir Koishekenov, Yejin Bang, Anthony Hartshorn, Alan Schelten, Cheng Zhang, Pascale Fung, Nicola Cancedda
| Challenge: | LLMs often use assertive language when making false claims, resulting in harm and loss of trust. |
| Approach: | They find that a mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone. |
| Outcome: | a new study shows that mismatch between semantic and verbal uncertainty is better predictor of hallucinations than semantic uncertainty alone. |