Hendrik Strobelt


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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann | Tosin Adewumi | Karmanya Aggarwal | Pawan Sasanka Ammanamanchi | Anuoluwapo Aremu | Antoine Bosselut | Khyathi Raghavi Chandu | Miruna-Adriana Clinciu | Dipanjan Das | Kaustubh Dhole | Wanyu Du | Esin Durmus | Ondřej Dušek | Chris Chinenye Emezue | Varun Gangal | Cristina Garbacea | Tatsunori Hashimoto | Yufang Hou | Yacine Jernite | Harsh Jhamtani | Yangfeng Ji | Shailza Jolly | Mihir Kale | Dhruv Kumar | Faisal Ladhak | Aman Madaan | Mounica Maddela | Khyati Mahajan | Saad Mahamood | Bodhisattwa Prasad Majumder | Pedro Henrique Martins | Angelina McMillan-Major | Simon Mille | Emiel van Miltenburg | Moin Nadeem | Shashi Narayan | Vitaly Nikolaev | Andre Niyongabo Rubungo | Salomey Osei | Ankur Parikh | Laura Perez-Beltrachini | Niranjan Ramesh Rao | Vikas Raunak | Juan Diego Rodriguez | Sashank Santhanam | João Sedoc | Thibault Sellam | Samira Shaikh | Anastasia Shimorina | Marco Antonio Sobrevilla Cabezudo | Hendrik Strobelt | Nishant Subramani | Wei Xu | Diyi Yang | Akhila Yerukola | Jiawei Zhou
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.

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LMdiff: A Visual Diff Tool to Compare Language Models
Hendrik Strobelt | Benjamin Hoover | Arvind Satyanaryan | Sebastian Gehrmann
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

While different language models are ubiquitous in NLP, it is hard to contrast their outputs and identify which contexts one can handle better than the other. To address this question, we introduce LMdiff, a tool that visually compares probability distributions of two models that differ, e.g., through finetuning, distillation, or simply training with different parameter sizes. LMdiff allows the generation of hypotheses about model behavior by investigating text instances token by token and further assists in choosing these interesting text instances by identifying the most interesting phrases from large corpora. We showcase the applicability of LMdiff for hypothesis generation across multiple case studies. A demo is available at http://lmdiff.net .


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exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
Benjamin Hoover | Hendrik Strobelt | Sebastian Gehrmann
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Large Transformer-based language models can route and reshape complex information via their multi-headed attention mechanism. Although the attention never receives explicit supervision, it can exhibit recognizable patterns following linguistic or positional information. Analyzing the learned representations and attentions is paramount to furthering our understanding of the inner workings of these models. However, analyses have to catch up with the rapid release of new models and the growing diversity of investigation techniques. To support analysis for a wide variety of models, we introduce exBERT, a tool to help humans conduct flexible, interactive investigations and formulate hypotheses for the model-internal reasoning process. exBERT provides insights into the meaning of the contextual representations and attention by matching a human-specified input to similar contexts in large annotated datasets. By aggregating the annotations of the matched contexts, exBERT can quickly replicate findings from literature and extend them to previously not analyzed models.


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GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann | Hendrik Strobelt | Alexander Rush
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by non-experts. In this work, we introduce GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across multiple sampling schemes. In a human-subjects study, we show that the annotation scheme provided by GLTR improves the human detection-rate of fake text from 54% to 72% without any prior training. GLTR is open-source and publicly deployed, and has already been widely used to detect generated outputs.


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Debugging Sequence-to-Sequence Models with Seq2Seq-Vis
Hendrik Strobelt | Sebastian Gehrmann | Michael Behrisch | Adam Perer | Hanspeter Pfister | Alexander Rush
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Neural attention-based sequence-to-sequence models (seq2seq) (Sutskever et al., 2014; Bahdanau et al., 2014) have proven to be accurate and robust for many sequence prediction tasks. They have become the standard approach for automatic translation of text, at the cost of increased model complexity and uncertainty. End-to-end trained neural models act as a black box, which makes it difficult to examine model decisions and attribute errors to a specific part of a model. The highly connected and high-dimensional internal representations pose a challenge for analysis and visualization tools. The development of methods to understand seq2seq predictions is crucial for systems in production settings, as mistakes involving language are often very apparent to human readers. For instance, a widely publicized incident resulted from a translation system mistakenly translating “good morning” into “attack them” leading to a wrongful arrest (Hern, 2017).