4 papers
Soft-Prompt Tuning for Fair and Efficient LLM Benchmark 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 ensuring that the reasoning trace both contributes to and faithfully…
A Family of LLMs Liberated from Static Vocabularies
Aleph Alpha, :, Adnen Abdessaied +35
Tokenization is a central component of natural language processing in current large language models (LLMs), enabling models to convert raw text into processable units. Although lea…
Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models
Björn Deiseroth, Björn Deiseroth, Max Henning Höth +3
Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic rather than verifiable evidence -- le…