From Bias to Balance: Navigating LLM Evaluations
Epikurious5 Des 2024

From Bias to Balance: Navigating LLM Evaluations

This research paper explores the challenges of evaluating Large Language Model (LLM) outputs and introduces EvalGen, a new interface designed to improve the alignment between LLM-generated evaluations and human preferences. EvalGen uses a mixed-initiative approach, combining automated LLM assistance with human feedback to generate and refine evaluation criteria and assertions. The study highlights a phenomenon called "criteria drift," where the process of grading outputs helps users define and refine their evaluation criteria. A qualitative user study demonstrates overall support for EvalGen, but also reveals complexities in aligning automated evaluations with human judgment, particularly regarding the subjective nature of evaluation and the iterative process of alignment. The authors conclude by discussing implications for future LLM evaluation assistants.


Episoder(15)

The LLM Performance Lab: Testing, Tuning, and Triumphs

The LLM Performance Lab: Testing, Tuning, and Triumphs

Both sources discuss building effective evaluation systems for Large Language Model (LLM) applications. The YouTube transcript details a case study where a real estate AI assistant, initially improved...

5 Des 202424min

RAGified: Smarter AI Conversations

RAGified: Smarter AI Conversations

Retrieval-Augmented Generation (RAG) applications, integrating information retrieval with language generation, are examined in this technical document. The paper explores methodologies for improving R...

5 Des 202414min

Beyond the Benchmark: Crafting the Future of AI Agent Evaluation and Optimization

Beyond the Benchmark: Crafting the Future of AI Agent Evaluation and Optimization

This research paper assesses the current state of AI agent benchmarking, highlighting critical flaws hindering real-world applicability. The authors identify shortcomings in existing benchmarks, inclu...

3 Des 202418min

From Prompt Engineering to AI Agent Frameworks: A Complete Guide

From Prompt Engineering to AI Agent Frameworks: A Complete Guide

This text presents a two-level learning roadmap for developing AI agents. Level 1 focuses on foundational knowledge, including generative AI, large language models (LLMs), prompt engineering, data han...

3 Des 20246min

Building Smarter AI: Practical Patterns for Leveraging Large Language Models

Building Smarter AI: Practical Patterns for Leveraging Large Language Models

Summary: This article details practical patterns for integrating large language models (LLMs) into systems and products. It covers seven key patterns: evaluations for performance measurement; retrieva...

3 Des 202429min

From Training to Thinking: Optimizing AI for Real-World Challenges

From Training to Thinking: Optimizing AI for Real-World Challenges

Summary: This research paper explores how to optimally increase the computational resources used by large language models (LLMs) during inference, rather than solely focusing on increasing model size ...

3 Des 202415min

BigFunctions: Simplifying BigQuery

BigFunctions: Simplifying BigQuery

BigFunctions is an open-source framework for creating and managing a catalog of BigQuery functions. It offers over 100 ready-to-use functions, enabling users to enhance their BigQuery data analysis. T...

24 Nov 20245min

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