Evaluating Alignment and Vulnerabilities in LLMs-as-Judges

  Рет қаралды 209

Arize AI

Arize AI

Күн бұрын

​This week’s paper, Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges, presents a comprehensive study of the performance of various LLMs acting as judges. The researchers leverage TriviaQA as a benchmark for assessing objective knowledge reasoning of LLMs and evaluate them alongside human annotations which they find to have a high inter-annotator agreement. The study includes nine judge models and nine exam-taker models - both base and instruction-tuned. They assess the judge models’ alignment across different model sizes, families, and judge prompts to answer questions about the strengths and weaknesses of this paradigm, and what potential biases it may hold.

Пікірлер
Breaking Down Meta's Llama 3 Herd of Models
47:29
Arize AI
Рет қаралды 768
Непосредственно Каха: сумка
0:53
К-Media
Рет қаралды 12 МЛН
Community Paper Reading: LLMs-as-Judges
28:49
Arize AI
Рет қаралды 165
Exploring Booking.com's Travel Agent
33:44
Arize AI
Рет қаралды 281
Стыдные вопросы про Китай / вДудь
3:07:50
вДудь
Рет қаралды 1,4 МЛН
Large Language Models explained briefly
8:48
3Blue1Brown
Рет қаралды 979 М.
Agents in the Wild: Geotab
33:07
Arize AI
Рет қаралды 227
Agent-as-a-Judge: Evaluate Agents with Agents
27:30
Arize AI
Рет қаралды 287
Building Agentic RAG Systems
19:32
Arize AI
Рет қаралды 1,8 М.