Deep Papers

Breaking Down EvalGen: Who Validates the Validators?

May 13, 2024

This podcast delves into the complexities of using Large Language Models for evaluation, highlighting the need for human validation in aligning LLM-generated evaluators with user preferences. Topics include developing criteria for acceptable LLM outputs, evaluating email responses, evolving evaluation criteria, template management, LLM validation, and the iterative process of building effective evaluation criteria.

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Quick takeaways

EvalGen aligns LLM outputs with user preferences for efficient evaluation process.

Regular refinement of evaluation criteria based on observations ensures accurate outcomes and user alignment.

Deep dives

Overview of Eval Gen Framework

The podcast episode discusses a paper called Cunvality Sivalities which introduces the Eval Gen framework aimed at aligning the evaluation criteria of assisted emails with user preferences. The framework introduces an open source tool called Eval Gen to address challenges related to evaluating LM outputs efficiently due to the high volume of queries and manual efforts involved. The framework focuses on transparency and alignment with user goals to enhance the evaluation process.

Introduction

2min

Evaluating Language Model Outputs Using the E-Valgen Framework

28min

Exploring the Evaluation Process of Data Sets in Email Responses

2min

Exploring the Evolution of Evaluation Criteria

3min

Discussion on Template Management and LMM Validation for Responses

6min

Exploring the Validation Process and Criteria Development for Evaluations

4min

Due to the cumbersome nature of human evaluation and limitations of code-based evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in evaluating LLM outputs. Yet LLM-generated evaluators often inherit the problems of the LLMs they evaluate, requiring further human validation.

This week’s paper explores EvalGen, a mixed-initative approach to aligning LLM-generated evaluation functions with human preferences. EvalGen assists users in developing both criteria acceptable LLM outputs and developing functions to check these standards, ensuring evaluations reflect the users’ own grading standards.

Read it on the blog: https://arize.com/blog/breaking-down-evalgen-who-validates-the-validators/

Paper: https://arxiv.org/abs/2404.12272

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Deep Papers

Breaking Down EvalGen: Who Validates the Validators?

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Overview of Eval Gen Framework

Criteria-Based Evaluation Workflow

Importance of Iterative Criteria Refinement

Enhancing User Interaction and Transparency

Remember Everything You Learn from Podcasts