7 citations · 9 across the 10 of their papers we have counts for
12 papers
MATCHA: Matching Text via Contrastive Semantic Alignment
Siran Li, Ece Sena Etoglu, Carsten Eickhoff +1
Reliable evaluation is essential for understanding large language model (LLM) performance, yet today's go-to metrics, namely token-overlap scores (e.g., ROUGE) and embedding-based…
When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?
Xinyu Zhou, Chang Jin, Carsten Eickhoff +2
Large language models (LLMs) rarely admit uncertainty, often producing fluent but misleading answers, rather than abstaining (i.e., refusing to answer). This weakness is even evide…
Navigating through the hidden embedding space: steering LLMs to improve mental health assessment
Federico Ravenda, Seyed Ali Bahrainian, Andrea Raballo +1
The rapid evolution of Large Language Models (LLMs) is transforming AI, opening new opportunities in sensitive and high-impact areas such as Mental Health (MH). Yet, despite these…
Benchmarking is Broken -- Don't Let AI be its Own Judge
Zerui Cheng, Stella Wohnig, Ruchika Gupta +13
The meteoric rise of AI, with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need…
Logit Reweighting for Topic-Focused Summarization
Joschka Braun, Bálint Mucsányi, Seyed Ali Bahrainian
Generating abstractive summaries that adhere to a specific topic remains a significant challenge for language models. While standard approaches, such as fine-tuning, are resource-i…
Understanding (Un)Reliability of Steering Vectors in Language Models
Joschka Braun, Carsten Eickhoff, David Krueger +2
Steering vectors are a lightweight method to control language model behavior by adding a learned bias to the activations at inference time. Although steering demonstrates promising…
Beyond Multiple Choice: Evaluating Steering Vectors for Summarization
Joschka Braun, Carsten Eickhoff, Seyed Ali Bahrainian
Steering vectors are a lightweight method for controlling text properties by adding a learned bias to language model activations at inference time. While predominantly studied for…
Enhancing Retrieval-Augmented Generation: A Study of Best Practices
Siran Li, Linus Stenzel, Carsten Eickhoff +1
Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produc…
Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice
Federico Ravenda, Seyed Ali Bahrainian, Andrea Raballo +2
In psychological practices, standardized questionnaires serve as essential tools for assessing mental health through structured, clinically-validated questions (i.e., items). While…
Controllable Topic-Focused Abstractive Summarization
Seyed Ali Bahrainian, Martin Jaggi, Carsten Eickhoff
Controlled abstractive summarization focuses on producing condensed versions of a source article to cover specific aspects by shifting the distribution of generated text towards a…
Neural Summarization of Electronic Health Records
Koyena Pal, Seyed Ali Bahrainian, Laura Mercurio +1
Hospital discharge documentation is among the most essential, yet time-consuming documents written by medical practitioners. The objective of this study was to automatically genera…
NEWTS: A Corpus for News Topic-Focused Summarization
Seyed Ali Bahrainian, Sheridan Feucht, Carsten Eickhoff
Text summarization models are approaching human levels of fidelity. Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or, profession…