Papers by Philipp Sadler
Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples (2023.eacl-main)
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| Challenge: | Using an extensional description of a visual input, we show that a model can produce referring expressions from visual inputs, whereas simpler baselines do not. |
| Approach: | They propose to use a visual dataset to generate referring expressions from visual inputs. |
| Outcome: | The proposed model achieves BLEU@1 score and sentence accuracy, whereas baselines do not. |
clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents (2023.emnlp-main)
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Kranti Chalamalasetti, Jana Götze, Sherzod Hakimov, Brielen Madureira, Philipp Sadler, David Schlangen
| Challenge: | Recent work suggests large language models can be understood as (simulators of) such agents. |
| Approach: | They propose a method for systematic evaluation of "Situated Language Understanding Agents" they propose implementing a framework for implementing rules to be played in "self-play" |
| Outcome: | The proposed model can be evaluated in game-like settings, the authors show . they show that the model can follow game-play instructions and perform better than existing models . |
Sharing the Cost of Success: A Game for Evaluating and Learning Collaborative Multi-Agent Instruction Giving and Following Policies (2024.lrec-main)
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| Challenge: | Recent advances in natural language processing have led to language model-based systems that do a good job at creating natural dialogue behaviour but are often verbose and brittle. |
| Approach: | They propose a game that requires two players to coordinate on vision and language observations. |
| Outcome: | The proposed game achieves high success rates when bootstrapped with heuristic partner behaviors that implement insights from the analysis of human-human interactions. |
Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers (2023.findings-acl)
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| Challenge: | In collaborative situations, communication is performed as signalling and recognizing, and the ability to act on these signals is crucial for future machine learning models to collaborate and interact with humans naturally. |
| Approach: | They propose to use a referential language game as an example of a collaborative joint activity to evaluate intra-episodic feedback given by a teacher. |
| Outcome: | The proposed model can generalize on aspects of scene complexity and perform better than providing only the initial statement. |
Playpen: An Environment for Exploring Learning From Dialogue Game Feedback (2025.emnlp-main)
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Nicola Horst, Davide Mazzaccara, Antonia Schmidt, Michael Sullivan, Filippo Momentè, Luca Franceschetti, Philipp Sadler, Sherzod Hakimov, Alberto Testoni, Raffaella Bernardi, Raquel Fernández, Alexander Koller, Oliver Lemon, David Schlangen, Mario Giulianelli, Alessandro Suglia
| Challenge: | In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed activities driven predominantly by verbal actions—can also serve as a source of feedback signals for learning. |
| Approach: | They introduce Playpen, an environment for off- and online learning through Dialogue Game self-play, and investigate a representative set of post-training methods: supervised fine-tuning, direct alignment and reinforcement learning with Group Relative Policy Optimization. |
| Outcome: | The proposed model improves performance on unseen instances, but negatively impacts other skills, while interactive learning shows balanced improvements without loss of skills. |