16 citations · 40 across the 17 of their papers we have counts for
3 papers · 2 filters
Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models
Polina Tsvilodub, Max Höth, Michael Franke +2
While modern large reasoning models (LRMs) excel at providing correct answers in many tasks, we provide additional evidence that they often struggle with a critical capability: kno…
Beyond Prompt and Parser Engineering: Soft-Prompt Tuning for Automated LLM Evaluation
Selen Erkan, Bastian Boll, Kristian Kersting +2
Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.g., on the model's ability to follow specific formatting requirements. This esp…
AtManRL: Towards Faithful Reasoning via Differentiable Attention Saliency
Max Henning Höth, Kristian Kersting, Björn Deiseroth +1
Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks, yet a reasoning trace may merely accompany the final answer rather than c…