26 citations · 50 across the 24 of their papers we have counts for
6 papers · 1 filter
Automatic Generation of Benchmarks and Reliable LLM Judgment for Code Tasks
Eitan Farchi, Shmulik Froimovich, Rami Katan +1
LLMs can be used in a variety of code related tasks such as translating from one programming language to another, implementing natural language requirements and code summarization.…
How Safe is Your Safety Metric? Automatic Concatenation Tests for Metric Reliability
Ora Nova Fandina, Leshem Choshen, Eitan Farchi +3
Consider a scenario where a harmfulness evaluation metric intended to filter unsafe responses from a Large Language Model. When applied to individual harmful prompt-response pairs,…
Generating Unseen Code Tests In Infinitum
Marcel Zalmanovici, Orna Raz, Eitan Farchi +1
Large Language Models (LLMs) are used for many tasks, including those related to coding. An important aspect of being able to utilize LLMs is the ability to assess their fitness fo…
Using Combinatorial Optimization to Design a High quality LLM Solution
Samuel Ackerman, Eitan Farchi, Rami Katan +1
We introduce a novel LLM based solution design approach that utilizes combinatorial optimization and sampling. Specifically, a set of factors that influence the quality of the solu…
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf +16
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In co…
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor +35
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (trai…