4 citations · 14 across the 14 of their papers we have counts for
10 papers · 1 filter
Knowledgeless Language Models: Suppressing Parametric Recall for Evidence-Grounded Language Modeling
Roi Cohen, Yvan Carré, Nick Lechtenbörger +5
Language models encode substantial factual knowledge in their parameters, which can lead to unreliable behavior when this knowledge is outdated, incomplete, or misaligned with the…
InFact: Informativeness Alignment for Improved LLM Factuality
Roi Cohen, Russa Biswas, Gerard de Melo
Factual completeness is a general term that captures how detailed and informative a factually correct text is. For instance, the factual sentence ``Barack Obama was born in the Uni…
MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
Ernests Lavrinovics, Russa Biswas, Katja Hose +1
Large Language Models (LLMs) have inherent limitations of faithfulness and factuality, commonly referred to as hallucinations. Several benchmarks have been developed that provide a…
Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality
Mike Zhang, Johannes Bjerva, Russa Biswas
We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by collecting them from large reasoning models and grounding them in knowledge grap…
Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective
Ernests Lavrinovics, Russa Biswas, Johannes Bjerva +1
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, chatbots, and others.…
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis
Yiyi Chen, Qiongxiu Li, Russa Biswas +1
Language Confusion is a phenomenon where Large Language Models (LLMs) generate text that is neither in the desired language, nor in a contextually appropriate language. This phenom…
Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attacks
Yiyi Chen, Russa Biswas, Heather Lent +1
Large Language Models (LLMs) are susceptible to malicious influence by cyber attackers through intrusions such as adversarial, backdoor, and embedding inversion attacks. In respons…
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
Russa Biswas, Jan Portisch, Heiko Paulheim +2
The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task o…
Entity Type Prediction in Knowledge Graphs using Embeddings
Russa Biswas, Radina Sofronova, Mehwish Alam +1
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, t…
Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
Russa Biswas, Mehwish Alam, Harald Sack
Representation Learning of words and Knowledge Graphs (KG) into low dimensional vector spaces along with its applications to many real-world scenarios have recently gained momentum…