92 citations · 247 across the 33 of their papers we have counts for
10 papers · 1 filter
Keep Guessing? When Considering Inference Scaling, Mind the Baselines
Gal Yona, Or Honovich, Omer Levy +1
Scaling inference compute in large language models (LLMs) through repeated sampling consistently increases the coverage (fraction of problems solved) as the number of samples incre…
Localizing Factual Inconsistencies in Attributable Text Generation
Arie Cattan, Paul Roit, Shiyue Zhang +5
There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existin…
Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs
Maxim Ifergan, Leshem Choshen, Roee Aharoni +2
The veracity of a factoid is largely independent of the language it is written in. However, language models are inconsistent in their ability to answer the same factual question ac…
DoubleDipper: Improving Long-Context LLMs via Context Recycling
Arie Cattan, Alon Jacovi, Alex Fabrikant +8
Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In this work, we propose DoubleDipper, a novel…
Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?
Gal Yona, Roee Aharoni, Mor Geva
We posit that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in natural language. For example, if the LLM is equally likely to output two…
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni +4
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training. It is often conjectured th…