Papers by Ece Takmaz
LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks (2025.acl-short)
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Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andre Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, Alberto Testoni
| Challenge: | Existing evaluations of NLP models with LLMs are based on human judgments . however, there are concerns about their validity and reproducibility in proprietary models . |
| Approach: | They evaluate 11 current LLMs for their ability to replicate annotations. they show substantial variance across models and datasets. |
| Outcome: | The proposed model can replicate human annotations on 20 NLP datasets and show substantial variance across models and datasets. |
Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind (2023.findings-acl)
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| Challenge: | Adaptation is a process in human communication by which a speaker tunes its language to that of a listener to achieve communicative success. |
| Approach: | They propose a visual-based referential game between a knowledgeable speaker and a listener with limited visual and linguistic experience to model this adaptation mechanism. |
| Outcome: | The proposed model improves on plug-and-play approaches to controlled language generation without finetuning the speaker’s underlying language model. |
Word Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation (2021.tacl-1)
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| Challenge: | Existing models for linguistic representations of words are based on information extracted from large text corpora, and the sensory-motor experiences humans have with the world play an important role in determining word meaning. |
| Approach: | They propose to use contextualized word representations to learn semantic representations of words that align with human semantic intuitions. |
| Outcome: | The proposed models are shown to be more efficient on concrete word pairs than on abstract ones. |
Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts (2020.emnlp-main)
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| Challenge: | Subsequent references exploit the common ground accumulated by the interlocutors and tend to be shorter and reuse expressions that were effective in previous mentions. |
| Approach: | They propose a model that generates first and subsequent references in visually grounded dialogue . they also implement a reference resolution system to assess the referring effectiveness . |
| Outcome: | The proposed model produces better, more effective referring utterances than one not grounded in the dialogue context. |
Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze (2020.emnlp-main)
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| Challenge: | a long tradition of cognitive studies shows that the interplay between language and vision is complex. |
| Approach: | They propose an approach to image description generation where visual processing is modelled sequentially. |
| Outcome: | The proposed model exploits gaze-driven attention to produce better descriptions . it sheds light on human cognitive processes by comparing different ways of aligning gaze with language production. |
The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue (P19-1)
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| Challenge: | Using the PhotoBook dataset, we investigate shared dialogue history accumulating during conversation . human interlocutors are known to collaboratively establish a shared repository of mutual information during a conversation - this common ground is then used to optimise understanding and communication efficiency. |
| Approach: | They propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context and previously established referring expressions. |
| Outcome: | The proposed model takes into account shared information accumulated in a reference chain and is important to resolve later descriptions. |
Describing Images Fast and Slow: Quantifying and Predicting the Variation in Human Signals during Visuo-Linguistic Processes (2024.eacl-long)
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| Challenge: | Existing models of visuo-linguistic variation are weak to moderately trained to capture such a variation in visual outputs. |
| Approach: | They use a corpus of Dutch image descriptions with eye-tracking data to investigate the nature of the variation in visuo-linguistic signals. |
| Outcome: | The proposed model lacks biases about what makes a stimulus complex for humans and what leads to variations in human outputs. |