Papers by Kristian Kersting
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes (2025.acl-long)
Copied to clipboard
Felix Friedrich, Katharina Hämmerl, Patrick Schramowski, Manuel Brack, Jindřich Libovický, Alexander Fraser, Kristian Kersting
| Challenge: | Text-to-image (T2I) generation models have great results in image quality, flexibility, and text alignment, but they suffer from substantial gender bias. |
| Approach: | They propose a benchmark to study gender bias in multilingual T2I models . they use multilingual prompts to account for grammatical differences influencing gender . |
| Outcome: | The proposed benchmark shows strong gender biases and language-specific differences across models. |
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2026.acl-long)
Copied to clipboard
Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma
| Challenge: | Large Language Models (LLMs) are stateless and limited by a finite context window, preventing them from maintaining knowledge across long conversations or evolving tasks. |
| Approach: | They propose a reinforcement learning framework that empowers LLMs to actively manage external memory through two specialized agents. |
| Outcome: | The proposed framework outperforms baselines and benchmarks across diverse question types, three benchmarks, and multiple model scales. |
Speaking Multiple Languages Affects the Moral Bias of Language Models (2023.findings-acl)
Copied to clipboard
Katharina Haemmerl, Bjoern Deiseroth, Patrick Schramowski, Jindřich Libovický, Constantin Rothkopf, Alexander Fraser, Kristian Kersting
| Challenge: | Pre-trained multilingual language models are often better on English than other languages . however, they are trained on varying amounts of data for each language . |
| Approach: | They apply the MORALDIRECTION framework to multilingual models and analyse their results . they find that PMLMs encode differing moral biases, but these do not correspond to cultural differences or commonalities in human opinions. |
| Outcome: | The proposed model captures moral norms from English and imposes them on other languages. |
Adaptable Adapters (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing work uses the same adapter architecture for every dataset regardless of the properties of the dataset or the amount of training data. |
| Approach: | They propose to use adaptable adapters to finetune lightweight neural network layers on top of pretrained weights. |
| Outcome: | The proposed adapters achieve on-par performances with the standard adapter architecture while using a considerably smaller number of adapter layers. |
Divergent Token Metrics: Measuring degradation to prune away LLM components – and optimize quantization (2024.naacl-long)
Copied to clipboard
Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting
| Challenge: | Large Language Models (LLMs) have reshaped natural language processing with impressive capabilities, but their ever-increasing size has raised concerns about their effective deployment and the need for LLM compression. |
| Approach: | This study introduces the Divergent Token Metrics (DTMs) that measure token divergences that allow deeper insights into the subtleties of model compression. |
| Outcome: | The proposed measures can identify outliers and improve performance in the sparseness of the LLMs. |
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models (2025.emnlp-main)
Copied to clipboard
Mehdi Ali, Manuel Brack, Max Lübbering, Elias Wendt, Abbas Goher Khan, Richard Rutmann, Alex Jude, Maurice Kraus, Alexander Arno Weber, Felix Stollenwerk, David Kaczér, Florian Mai, Lucie Flek, Rafet Sifa, Nicolas Flores-Herr, Joachim Koehler, Patrick Schramowski, Michael Fromm, Kristian Kersting
| Challenge: | Existing open-source multilingual datasets rely on heuristic filtering methods restricting both their cross-lingual transferability and scalability. |
| Approach: | They propose a systematic approach that curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. |
| Outcome: | Evaluated empirically across 35 languages, the proposed approach outperforms current heuristic filtering methods like Fineweb2 and improves model training quality and retention rates. |
T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings (2024.emnlp-main)
Copied to clipboard
| Challenge: | Tokenizers are crucial for encoding information in Large Language Models, but their development has stagnated. |
| Approach: | They propose a tokenizer that embeds words through sparse activation patterns over character triplets . they show competitive downstream performance with a parameter reduction of more than 85% . |
| Outcome: | The proposed approach achieves competitive downstream performance with a parameter reduction of more than 85% on embedding layers. |
SLR: Automated Synthesis for Scalable Logical Reasoning (2026.acl-long)
Copied to clipboard
Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia Wüst, Hikaru Shindo, Rupert Mitchell, Tim Woydt, Patrick Schramowski, Wolfgang Stammer, Kristian Kersting
| Challenge: | Existing benchmarks intended to evaluate reasoning capabilities emphasize deductive reasoning, where conclusions necessarily follow from given premises. |
| Approach: | They propose an end-to-end framework for systematic evaluation and training of Large Language Models via Scalable Logical Reasoning. |
| Outcome: | The proposed framework doubles Llama-3-8B accuracy on SLR-Bench, achieving parity with Gemini-Flash-Thinking at a fraction of computational cost. |
METok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Video Large Language Models (VLLMs) have significantly enhanced their ability to understand video content. |
| Approach: | They propose a training-free, Multi-stage Event-based Token compression framework that eliminates redundant visual tokens across three critical stages . |
| Outcome: | The proposed framework reduces FLOPs and KV Cache memory consumption while maintaining comparable or even superior accuracy. |
STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond (2025.acl-long)
Copied to clipboard
| Challenge: | Existing work treats critical text assessment as a black box problem, limiting interpretability and human-AI collaboration. |
| Approach: | They propose a framework to model critical text assessment as an explicit, step-wise reasoning process. |
| Outcome: | The proposed framework breaks down assessment into a graph of interconnected reasoning steps drawing on causality theory. |