activity
20242026
most citedImproving Semantic Understanding in Speech Language Models via Brain-tuning

4 citations · 8 across the 34 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

Fork-Think with Confidence

Zena Al-Khalili, Rafi Hakim, Dietrich Klakow +1

Parallel thinking has enjoyed great success for boosting LLM performance on reasoning tasks without the need for any re-training. However, existing methods follow a think-first-the…

cs.LG2026

On Optimizing Multimodal Jailbreaks for Spoken Language Models

Aravind Krishnan, Karolina Stańczak, Dietrich Klakow

As Spoken Language Models (SLMs) integrate speech and text modalities, they inherit the safety vulnerabilities of their LLM backbone while introducing an expanded attack surface. S…

cs.LG2025

Decoding Partial Differential Equations: Cross-Modal Adaptation of Decoder-only Models to PDEs

Paloma García-de-Herreros, Philipp Slusallek, Dietrich Klakow +1

While large language models are primarily used on natural language tasks, they have also shown great promise when adapted to new modalities, e.g., for scientific machine learning t…

cs.LG2025

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches

Israel Abebe Azime, Deborah D. Kanubala, Tejumade Afonja +4

Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks, such as loan approvals. While their applications expand across domains, LLMs struggle t…

cs.LG2025

IGC: Integrating a Gated Calculator into an LLM to Solve Arithmetic Tasks Reliably and Efficiently

Florian Dietz, Dietrich Klakow

Solving arithmetic tasks is a simple and fundamental skill, yet modern Large Language Models (LLMs) have great difficulty with them. We introduce the Integrated Gated Calculator (I…

cs.LG2024

Block-Operations: Using Modular Routing to Improve Compositional Generalization

Florian Dietz, Dietrich Klakow

We explore the hypothesis that poor compositional generalization in neural networks is caused by difficulties with learning effective routing. To solve this problem, we propose the…

cs.LG2024

Comgra: A Tool for Analyzing and Debugging Neural Networks

Florian Dietz, Sophie Fellenz, Dietrich Klakow +1

Neural Networks are notoriously difficult to inspect. We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations o…