9 citations · 26 across the 19 of their papers we have counts for
7 papers · 1 filter
Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation
Vera Neplenbroek, Arianna Bisazza, Raquel Fernández
Recent generative large language models (LLMs) show remarkable performance in non-English languages, but when prompted in those languages they tend to express higher harmful social…
Pointwise Mutual Information as a Performance Gauge for Retrieval-Augmented Generation
Tianyu Liu, Jirui Qi, Paul He +3
Recent work suggests that large language models enhanced with retrieval-augmented generation are easily influenced by the order, in which the retrieved documents are presented to t…
Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses
Gabriele Sarti, Tommaso Caselli, Malvina Nissim +1
Rebuses are puzzles requiring constrained multi-step reasoning to identify a hidden phrase from a set of images and letters. In this work, we introduce a large collection of verbal…
MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs
Vera Neplenbroek, Arianna Bisazza, Raquel Fernández
Generative large language models (LLMs) have been shown to exhibit harmful biases and stereotypes. While safety fine-tuning typically takes place in English, if at all, these model…
The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models
Xinyi Chen, Baohao Liao, Jirui Qi +4
Following multiple instructions is a crucial ability for large language models (LLMs). Evaluating this ability comes with significant challenges: (i) limited coherence between mult…
Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation
Jirui Qi, Gabriele Sarti, Raquel Fernández +1
Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain. Recently, self-citation promp…