Ivan Titov
Professor
Works on natural language processing and machine learning, including sparse and differentiable structure in models, knowledge editing in LLMs and interpretability.
Ivan Titov's lab at Edinburgh works on large language models, knowledge, QA & retrieval and trustworthy & safe ML. 31 papers since 2023, 19 at top venues, mostly EMNLP and ICLR.
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 61%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 6 papers2026 · T1 1 · collecting
- 11 papers2025 · T1 10
- 6 papers2024 · T1 4
- 8 papers2023 · T1 4
T1 venuesOtherVenues: EMNLP, ICLR, ACL, Findings of ACL, Findings of EMNLP
Research focuspaper titles that use each topic's words
- Large language models13 papers
- editing1 paper
- Knowledge, QA & retrieval0 papers
- Trustworthy & safe ML0 papers
- differentiable0 papers
Paper list
From DBLP.
20266 papers
- Findings of EACLAnalyzing LLM Instruction Optimization for Tabular Fact Verification (opens in a new tab)
- Trans. Assoc. Comput. LinguisticsAnthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing (opens in a new tab)
- Findings of ACLControlling What You Share: Assessing Language Model Adherence to Privacy Preferences (opens in a new tab)
- EACLFinding Culture-Sensitive Neurons in Vision-Language Models (opens in a new tab)
- Findings of EACLMitigating Copy Bias in In-Context Learning through Neuron Pruning (opens in a new tab)
- ICLR (T1)Truthful or Fabricated? Using Causal Attribution to Mitigate Reward Hacking in Explanations (opens in a new tab)
202511 papers
- NeurIPS (T1)A Controllable Examination for Long-Context Language Models (opens in a new tab)
- ACL (T1)Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models (opens in a new tab)
- IJCNLP-AACLEnhancing Long Document Long Form Summarisation with Self-Planning (opens in a new tab)
- EMNLP (T1)Enhancing RLHF with Human Gaze Modeling (opens in a new tab)
- COLING (T1)Explanation Regularisation through the Lens of Attributions (opens in a new tab)
- ICML (T1)Joint Localization and Activation Editing for Low-Resource Fine-Tuning (opens in a new tab)
- ICLR (T1)Language Agents Meet Causality - Bridging LLMs and Causal World Models (opens in a new tab)
- ICLR (T1)Layerwise Recurrent Router for Mixture-of-Experts (opens in a new tab)
- EMNLP (T1)M-Wanda: Improving One-Shot Pruning for Multilingual LLMs (opens in a new tab)
- ICLR (T1)Post-hoc Reward Calibration: A Case Study on Length Bias (opens in a new tab)
- ICLR (T1)What's New in My Data? Novelty Exploration via Contrastive Generation (opens in a new tab)
20246 papers
- ICML (T1)Autoencoding Conditional Neural Processes for Representation Learning (opens in a new tab)
- Findings of ACLCache & Distil: Optimising API Calls to Large Language Models (opens in a new tab)
- Findings of ACLGeneralisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks (opens in a new tab)
- ACL (T1)SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation (opens in a new tab)
- EMNLP (T1)Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations (opens in a new tab)
- ACL (T1)Unlearning Traces the Influential Training Data of Language Models (opens in a new tab)
20238 papers
- EACLCompositional Generalisation with Structured Reordering and Fertility Layers (opens in a new tab)
- Findings of EMNLPCompositional Generalization for Data-to-Text Generation (opens in a new tab)
- ACL (T1)Compositional Generalization without Trees using Multiset Tagging and Latent Permutations (opens in a new tab)
- EMNLP (T1)Cross-Modal Conceptualization in Bottleneck Models (opens in a new tab)
- EMNLP (T1)Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation (opens in a new tab)
- Findings of EMNLPOn the Transferability of Visually Grounded PCFGs (opens in a new tab)
- Findings of EMNLPSubspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training (opens in a new tab)
- NeurIPS (T1)Theoretical and Practical Perspectives on what Influence Functions Do (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to EdinburghFall 2027
Not announced yet
Informatics PhD
- Round
- Rolling (funding consideration)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Not yet confirmed for this intake.
Similar labsnearest by research summary
- GBEdinburgh
Pasquale Minervini
- Knowledge, QA & retrieval
- HKCUHK
Wai Lam
- Knowledge, QA & retrieval
- USUCLA
Vwani Roychowdhury
- Language & society
- HKCUHK
Michael Rung Tsong Lyu
- Large language models
- CNTsinghua
Qingyao Ai
- Large language models
- USNYU
Ernest Davis
- Reasoning & neuro-symbolic AI
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- DBLP
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