Ian Tenney

I am a Staff Research Scientist on the People + AI Research (PAIR) team at Google DeepMind. My group focuses on interpretability for large langauge models (LLMs), including visualization tools, attribution methods, and intrinsic analysis (a.k.a. BERTology) of model representations. Through these, we aim to answer questions like:

Why did a model make this particular prediction?
What kind of knowledge is stored in the parameters, and how is it represented and reasoned about?
How can we build our own mental models of how - and when - AI works?

Among other things, I am a co-creator of the Learning Interpretability Tool (LIT) and author of BERT Rediscovers the Classical NLP Pipeline.

Previously, I’ve taught an NLP course at UC Berkeley School of Information. In a past life I was a physicist, studying ultrafast molecular and optical physics in the lab of Philip H. Bucksbaum at Stanford / SLAC.

When I’m not behind a computer I enjoy hiking and photography; you can find some of it here.

Contact: "if" + lastname + "@gmail.com" (or @google.com)

news

Jul 19, 2025	I’m helping to organize the first Workshop on Actionable Interpretability at ICML 2025, aiming to foster discussions on how we can better apply findings and methods from interpretability research to the practical enterprise of LLMs.
Jan 22, 2025	Pleased to share that our recent TDA work was accepted to ICLR 2025! Check out the preprint here: Scalable Influence and Fact Tracing for Large Language Model Pretraining (Chang et al. 2025)
Dec 13, 2024	New blog post & preprint on Scaling Training Data Attribution to understand what data an LLM learned from during open-domain pretraining. We’ve also released the dataset along with a web-based demo to explore influential examples for a variety of queries.

projects

Interpretability & Visualization Tools

Visualize, inspect, and debug behavior of LLMs and other ML models.

Training Data Attribution

Understanding model behavior by finding training examples that influence a particular prediction.

Probing & BERTology

Understanding linguistic structure in pre-trained language models, such as ELMo and BERT.

selected publications

Scalable Influence and Fact Tracing for Large Language Model Pretraining

Tyler A. Chang, Dheeraj Rajagopal, Tolga Bolukbasi, Lucas Dixon, and Ian Tenney

ICLR, 2025

Abstract arXiv ICLR 2025 Bluesky Thread Blog Code Dataset Demo

Training data attribution (TDA) methods aim to attribute model outputs back to specific training examples, and the application of these methods to large language model (LLM) outputs could significantly advance model transparency and data curation. However, it has been challenging to date to apply these methods to the full scale of LLM pretraining. In this paper, we refine existing gradient-based methods to work effectively at scale, allowing us to retrieve influential examples for an 8B-parameter language model from a pretraining corpus of over 160B tokens with no need for subsampling or pre-filtering. Our method combines several techniques, including optimizer state correction, a task-specific Hessian approximation, and normalized encodings, which we find to be critical for performance at scale. In quantitative evaluations on a fact tracing task, our method performs best at identifying examples that influence model predictions, but classical, model-agnostic retrieval methods such as BM25 still perform better at finding passages which explicitly contain relevant facts. These results demonstrate a misalignment between factual attribution and causal influence. With increasing model size and training tokens, we find that influence more closely aligns with attribution. Finally, we examine different types of examples identified as influential by our method, finding that while many directly entail a particular fact, others support the same output by reinforcing priors on relation types, common entities, and names.
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models

Minsuk Kahng, Ian Tenney, Mahima Pushkarna, Michael Xieyang Liu, James Wexler, Emily Reif, Krystal Kallarackal, Minsuk Chang, Michael Terry, and Lucas Dixon

IEEE Transactions on Visualization and Computer Graphics, 2024

Abstract arXiv IEEE VIS 2024 Thread Blog Code Demo

Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs). However, analyzing the results from this evaluation approach raises scalability and interpretability challenges. In this paper, we present LLM Comparator, a novel visual analytics tool for interactively analyzing results from automatic side-by-side evaluation. The tool supports interactive workflows for users to understand when and why a model performs better or worse than a baseline model, and how the responses from two models are qualitatively different. We iteratively designed and developed the tool by closely working with researchers and engineers at a large technology company. This paper details the user challenges we identified, the design and development of the tool, and an observational study with participants who regularly evaluate their models.
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs

Kelvin Guu, Albert Webson, Ellie Pavlick, Lucas Dixon, Ian Tenney, and Tolga Bolukbasi

arXiv preprint, 2023

Abstract arXiv Thread

Training data attribution (TDA) methods offer to trace a model’s prediction on any given example back to specific influential training examples. Existing approaches do so by assigning a scalar influence score to each training example, under a simplifying assumption that influence is additive. But in reality, we observe that training examples interact in highly non-additive ways due to factors such as inter-example redundancy, training order, and curriculum learning effects. To study such interactions, we propose Simfluence, a new paradigm for TDA where the goal is not to produce a single influence score per example, but instead a training run simulator: the user asks, “If my model had trained on example z1, then z2, ..., then zn, how would it behave on ztest?”; the simulator should then output a simulated training run, which is a time series predicting the loss on ztest at every step of the simulated run. This enables users to answer counterfactual questions about what their model would have learned under different training curricula, and to directly see where in training that learning would occur. We present a simulator, Simfluence-Linear, that captures non-additive interactions and is often able to predict the spiky trajectory of individual example losses with surprising fidelity. Furthermore, we show that existing TDA methods such as TracIn and influence functions can be viewed as special cases of Simfluence-Linear. This enables us to directly compare methods in terms of their simulation accuracy, subsuming several prior TDA approaches to evaluation. In experiments on large language model (LLM) fine-tuning, we show that our method predicts loss trajectories with much higher accuracy than existing TDA methods (doubling Spearman’s correlation and reducing mean-squared error by 75%) across several tasks, models, and training methods.
The MultiBERTs: BERT Reproductions for Robustness Analysis

Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D’Amour, Tal Linzen, Jasmijn Bastings, Iulia Turc, Jacob Eisenstein, Dipanjan Das, and Ellie Pavlick

ICLR (spotlight), 2022

Abstract arXiv ICLR 2022 Code

Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the particular instance of the model), it is not always clear whether they hold for the more general procedure which includes the architecture, training data, initialization scheme, and loss function. Recent work has shown that repeating the pre-training process can lead to substantially different performance, suggesting that an alternative strategy is needed to make principled statements about procedures. To enable researchers to draw more robust conclusions, we introduce MultiBERTs, a set of 25 BERT-Base checkpoints, trained with similar hyper-parameters as the original BERT model but differing in random weight initialization and shuffling of training data. We also define the Multi-Bootstrap, a non-parametric bootstrap method for statistical inference designed for settings where there are multiple pre-trained models and limited test data. To illustrate our approach, we present a case study of gender bias in coreference resolution, in which the Multi-Bootstrap lets us measure effects that may not be detected with a single checkpoint. The models and statistical library are available online, along with an additional set of 140 intermediate checkpoints captured during pre-training to facilitate research on learning dynamics.
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan

In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020

Abstract arXiv EMNLP 2020 Blog Code Demo Poster Website

We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why did my model make this prediction? When does it perform poorly? What happens under a controlled change in the input? LIT integrates local explanations, aggregate analysis, and counterfactual generation into a streamlined, browser-based interface to enable rapid exploration and error analysis. We include case studies for a diverse set of workflows, including exploring counterfactuals for sentiment analysis, measuring gender bias in coreference systems, and exploring local behavior in text generation. LIT supports a wide range of models—including classification, seq2seq, and structured prediction—and is highly extensible through a declarative, framework-agnostic API. LIT is under active development, with code and full documentation available at https://github.com/pair-code/lit.
BERT Rediscovers the Classical NLP Pipeline

Ian Tenney, Dipanjan Das, and Ellie Pavlick

In Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Abstract arXiv ACL 2019 Code Poster

Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations.
What do you learn from context? Probing for sentence structure in contextualized word representations

Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Sam Bowman, Dipanjan Das, and Ellie Pavlick

In International Conference on Learning Representations, 2019

Abstract arXiv ICLR 2019 Code Poster

Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-sentence tasks derived from the traditional structured NLP pipeline. We probe word-level contextual representations from four recent models and investigate how they encode sentence structure across a range of syntactic, semantic, local, and long-range phenomena. We find that existing models trained on language modeling and translation produce strong representations for syntactic phenomena, but only offer comparably small improvements on semantic tasks over a non-contextual baseline.