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Evaluation Benchmarks

Introduction

TruLens relies on feedback functions to score the performance of LLM apps, which are implemented across a variety of LLMs and smaller models. The numerical scoring scheme adopted by TruLens' feedback functions is intuitive for generating aggregated results from eval runs that are easy to interpret and visualize across different applications of interest. However, it begs the question how trustworthy these scores actually are, given they are at their core next-token-prediction-style generation from meticulously designed prompts.

Consequently, these feedback functions face typical large language model (LLM) challenges in rigorous production environments, including prompt sensitivity and non-determinism, especially when incorporating Mixture-of-Experts and model-as-a-service solutions like those from OpenAI, Mistral, and others. Drawing inspiration from works on Judging LLM-as-a-Judge, we outline findings from our analysis of feedback function performance against task-aligned benchmark data. To accomplish this, we first need to align feedback function tasks to relevant benchmarks in order to gain access to large scale ground truth data for the feedback functions. We then are able to easily compute metrics across a variety of implementations and models.

Groundedness

Methods

Observing that many summarization benchmarks, such as those found at SummEval, use human annotation of numerical scores, we propose to frame the problem of evaluating groundedness tasks as evaluating a summarization system. In particular, we generate test cases from SummEval.

SummEval is one of the datasets dedicated to automated evaluations on summarization tasks, which are closely related to the groundedness evaluation in RAG with the retrieved context (i.e. the source) and response (i.e. the summary). It contains human annotation of numerical score (1 to 5) comprised of scoring from 3 human expert annotators and 5 crowd-sourced annotators. There are 16 models being used for generation in total for 100 paragraphs in the test set, so there are a total of 16,000 machine-generated summaries. Each paragraph also has several human-written summaries for comparative analysis.

For evaluating groundedness feedback functions, we compute the annotated "consistency" scores, a measure of whether the summarized response is factually consisntent with the source texts and hence can be used as a proxy to evaluate groundedness in our RAG triad, and normalized to 0 to 1 score as our expected_score and to match the output of feedback functions.

See the code.

Results

Feedback Function Base Model SummEval MAE Latency Total Cost
Llama-3 70B Instruct 0.054653 12.184049 0.000005
Arctic Instruct 0.076393 6.446394 0.000003
GPT 4o 0.057695 6.440239 0.012691
Mixtral 8x7B Instruct 0.340668 4.89267 0.000264

Comprehensiveness

Methods

This notebook follows an evaluation of a set of test cases generated from human annotated datasets. In particular, we generate test cases from MeetingBank to evaluate our comprehensiveness feedback function.

MeetingBank is one of the datasets dedicated to automated evaluations on summarization tasks, which are closely related to the comprehensiveness evaluation in RAG with the retrieved context (i.e. the source) and response (i.e. the summary). It contains human annotation of numerical score (1 to 5).

For evaluating comprehensiveness feedback functions, we compute the annotated "informativeness" scores, a measure of how well the summaries capture all the main points of the meeting segment. A good summary should contain all and only the important information of the source., and normalized to 0 to 1 score as our expected_score and to match the output of feedback functions.

See the code.

Results

Feedback Function Base Model Meetingbank MAE
GPT 3.5 Turbo 0.170573
GPT 4 Turbo 0.163199
GPT 4o 0.183592