Evaluating generative AI fashions with Amazon Nova LLM-as-a-Choose on Amazon SageMaker AI

Evaluating the efficiency of massive language fashions (LLMs) goes past statistical metrics like perplexity or bilingual analysis understudy (BLEU) scores. For many real-world generative AI situations, it’s essential to know whether or not a mannequin is producing higher outputs than a baseline or an earlier iteration. That is particularly vital for purposes akin to summarization, content material era, or clever brokers the place subjective judgments and nuanced correctness play a central position.

As organizations deepen their deployment of those fashions in manufacturing, we’re experiencing an growing demand from clients who wish to systematically assess mannequin high quality past conventional analysis strategies. Present approaches like accuracy measurements and rule-based evaluations, though useful, can’t absolutely handle these nuanced evaluation wants, significantly when duties require subjective judgments, contextual understanding, or alignment with particular enterprise necessities. To bridge this hole, LLM-as-a-judge has emerged as a promising method, utilizing the reasoning capabilities of LLMs to judge different fashions extra flexibly and at scale.

Right this moment, we’re excited to introduce a complete method to mannequin analysis by way of the Amazon Nova LLM-as-a-Choose functionality on Amazon SageMaker AI, a completely managed Amazon Net Providers (AWS) service to construct, prepare, and deploy machine studying (ML) fashions at scale. Amazon Nova LLM-as-a-Choose is designed to ship sturdy, unbiased assessments of generative AI outputs throughout mannequin households. Nova LLM-as-a-Choose is accessible as optimized workflows on SageMaker AI, and with it, you can begin evaluating mannequin efficiency in opposition to your particular use circumstances in minutes. Not like many evaluators that exhibit architectural bias, Nova LLM-as-a-Choose has been rigorously validated to stay neutral and has achieved main efficiency on key choose benchmarks whereas carefully reflecting human preferences. With its distinctive accuracy and minimal bias, it units a brand new normal for credible, production-grade LLM analysis.

Nova LLM-as-a-Choose functionality offers pairwise comparisons between mannequin iterations, so you may make data-driven choices about mannequin enhancements with confidence.

How Nova LLM-as-a-Choose was educated

Nova LLM-as-a-Choose was constructed by way of a multistep coaching course of comprising supervised coaching and reinforcement studying levels that used public datasets annotated with human preferences. For the proprietary part, a number of annotators independently evaluated 1000’s of examples by evaluating pairs of various LLM responses to the identical immediate. To confirm consistency and equity, all annotations underwent rigorous high quality checks, with ultimate judgments calibrated to mirror broad human consensus somewhat than a person viewpoint.

The coaching knowledge was designed to be each numerous and consultant. Prompts spanned a variety of classes, together with real-world information, creativity, coding, arithmetic, specialised domains, and toxicity, so the mannequin may consider outputs throughout many real-world situations. Coaching knowledge included knowledge from over 90 languages and is primarily composed of English, Russian, Chinese language, German, Japanese, and Italian.Importantly, an inner bias research evaluating over 10,000 human-preference judgments in opposition to 75 third-party fashions confirmed that Amazon Nova LLM-as-a-Choose reveals solely a 3% mixture bias relative to human annotations. Though it is a vital achievement in decreasing systematic bias, we nonetheless suggest occasional spot checks to validate vital comparisons.

Within the following determine, you may see how the Nova LLM-as-a-Choose bias compares to human preferences when evaluating Amazon Nova outputs in comparison with outputs from different fashions. Right here, bias is measured because the distinction between the choose’s choice and human choice throughout 1000’s of examples. A optimistic worth signifies the choose barely favors Amazon Nova fashions, and a damaging worth signifies the alternative. To quantify the reliability of those estimates, 95% confidence intervals have been computed utilizing the usual error for the distinction of proportions, assuming unbiased binomial distributions.

Amazon Nova LLM-as-a-Choose achieves superior efficiency amongst analysis fashions, demonstrating robust alignment with human judgments throughout a variety of duties. For instance, it scores 45% accuracy on JudgeBench (in comparison with 42% for Meta J1 8B) and 68% on PPE (versus 60% for Meta J1 8B). The info from Meta’s J1 8B was pulled from Incentivizing Considering in LLM-as-a-Choose by way of Reinforcement Studying.

These outcomes spotlight the energy of Amazon Nova LLM-as-a-Choose in chatbot-related evaluations, as proven within the PPE benchmark. Our benchmarking follows present greatest practices, reporting reconciled outcomes for positionally swapped responses on JudgeBench, CodeUltraFeedback, Eval Bias, and LLMBar, whereas utilizing single-pass outcomes for PPE.

On this submit, we current a streamlined method to implementing Amazon Nova LLM-as-a-Choose evaluations utilizing SageMaker AI, decoding the ensuing metrics, and making use of this course of to enhance your generative AI purposes.

Overview of the analysis workflow

The analysis course of begins by making ready a dataset through which every instance features a immediate and two various mannequin outputs. The JSONL format appears to be like like this:

{
   "immediate":"Clarify photosynthesis.",
   "response_A":"Reply A...",
   "response_B":"Reply B..."
}
{
   "immediate":"Summarize the article.",
   "response_A":"Reply A...",
   "response_B":"Reply B..."
}

After making ready this dataset, you employ the given SageMaker analysis recipe, which configures the analysis technique, specifies which mannequin to make use of because the choose, and defines the inference settings akin to temperature and top_p.

The analysis runs inside a SageMaker coaching job utilizing pre-built Amazon Nova containers. SageMaker AI provisions compute sources, orchestrates the analysis, and writes the output metrics and visualizations to Amazon Easy Storage Service (Amazon S3).

When it’s full, you may obtain and analyze the outcomes, which embrace choice distributions, win charges, and confidence intervals.

Understanding how Amazon Nova LLM-as-a-Choose works

The Amazon Nova LLM-as-a-Choose makes use of an analysis technique known as binary general choice choose. The binary general choice choose is a technique the place a language mannequin compares two outputs aspect by aspect and picks the higher one or declares a tie. For every instance, it produces a transparent choice. If you mixture these judgments over many samples, you get metrics like win price and confidence intervals. This method makes use of the mannequin’s personal reasoning to evaluate qualities like relevance and readability in a simple, constant means.

• This choose mannequin is supposed to offer low-latency normal general preferences in conditions the place granular suggestions isn’t vital
• The output of this mannequin is one in all [[A>B]] or [[B>A]]
• Use circumstances for this mannequin are primarily these the place automated, low-latency, normal pairwise preferences are required, akin to automated scoring for checkpoint choice in coaching pipelines

Understanding Amazon Nova LLM-as-a-Choose analysis metrics

When utilizing the Amazon Nova LLM-as-a-Choose framework to match outputs from two language fashions, SageMaker AI produces a complete set of quantitative metrics. You need to use these metrics to evaluate which mannequin performs higher and the way dependable the analysis is. The outcomes fall into three primary classes: core choice metrics, statistical confidence metrics, and normal error metrics.

The core choice metrics report how typically every mannequin’s outputs have been most well-liked by the choose mannequin. The a_scores metric counts the variety of examples the place Mannequin A was favored, and b_scores counts circumstances the place Mannequin B was chosen as higher. The ties metric captures situations through which the choose mannequin rated each responses equally or couldn’t establish a transparent choice. The inference_error metric counts circumstances the place the choose couldn’t generate a legitimate judgment as a result of malformed knowledge or inner errors.

The statistical confidence metrics quantify how probably it’s that the noticed preferences mirror true variations in mannequin high quality somewhat than random variation. The winrate stories the proportion of all legitimate comparisons through which Mannequin B was most well-liked. The lower_rate and upper_rate outline the decrease and higher bounds of the 95% confidence interval for this win price. For instance, a winrate of 0.75 with a confidence interval between 0.60 and 0.85 means that, even accounting for uncertainty, Mannequin B is persistently favored over Mannequin A. The rating subject typically matches the rely of Mannequin B wins however may also be personalized for extra advanced analysis methods.

The normal error metrics present an estimate of the statistical uncertainty in every rely. These embrace a_scores_stderr, b_scores_stderr, ties_stderr, inference_error_stderr, andscore_stderr. Smaller normal error values point out extra dependable outcomes. Bigger values can level to a necessity for added analysis knowledge or extra constant immediate engineering.

Decoding these metrics requires consideration to each the noticed preferences and the boldness intervals:

• If the winrate is considerably above 0.5 and the boldness interval doesn’t embrace 0.5, Mannequin B is statistically favored over Mannequin A.
• Conversely, if the winrate is under 0.5 and the boldness interval is absolutely under 0.5, Mannequin A is most well-liked.
• When the boldness interval overlaps 0.5, the outcomes are inconclusive and additional analysis is really helpful.
• Excessive values in inference_error or massive normal errors recommend there might need been points within the analysis course of, akin to inconsistencies in immediate formatting or inadequate pattern measurement.

The next is an instance metrics output from an analysis run:

{
  "a_scores": 16.0,
  "a_scores_stderr": 0.03,
  "b_scores": 10.0,
  "b_scores_stderr": 0.09,
  "ties": 0.0,
  "ties_stderr": 0.0,
  "inference_error": 0.0,
  "inference_error_stderr": 0.0,
  "rating": 10.0,
  "score_stderr": 0.09,
  "winrate": 0.38,
  "lower_rate": 0.23,
  "upper_rate": 0.56
}

On this instance, Mannequin A was most well-liked 16 instances, Mannequin B was most well-liked 10 instances, and there have been no ties or inference errors. The winrate of 0.38 signifies that Mannequin B was most well-liked in 38% of circumstances, with a 95% confidence interval starting from 23% to 56%. As a result of the interval contains 0.5, this final result suggests the analysis was inconclusive, and extra knowledge is perhaps wanted to make clear which mannequin performs higher general.

These metrics, routinely generated as a part of the analysis course of, present a rigorous statistical basis for evaluating fashions and making data-driven choices about which one to deploy.

Answer overview

This resolution demonstrates the way to consider generative AI fashions on Amazon SageMaker AI utilizing the Nova LLM-as-a-Choose functionality. The supplied Python code guides you thru the whole workflow.

First, it prepares a dataset by sampling questions from SQuAD and producing candidate responses from Qwen2.5 and Anthropic’s Claude 3.7. These outputs are saved in a JSONL file containing the immediate and each responses.

We accessed Anthropic’s Claude 3.7 Sonnet in Amazon Bedrock utilizing the bedrock-runtime shopper. We accessed Qwen2.5 1.5B utilizing a SageMaker hosted Hugging Face endpoint.

Subsequent, a PyTorch Estimator launches an analysis job utilizing an Amazon Nova LLM-as-a-Choose recipe. The job runs on GPU situations akin to ml.g5.12xlarge and produces analysis metrics, together with win charges, confidence intervals, and choice counts. Outcomes are saved to Amazon S3 for evaluation.

Lastly, a visualization perform renders charts and tables, summarizing which mannequin was most well-liked, how robust the choice was, and the way dependable the estimates are. By means of this end-to-end method, you may assess enhancements, observe regressions, and make data-driven choices about deploying generative fashions—all with out guide annotation.

Stipulations

You have to full the next conditions earlier than you may run the pocket book:

1. Make the next quota enhance requests for SageMaker AI. For this use case, it is advisable request a minimal of 1 g5.12xlarge occasion. On the Service Quotas console, request the next SageMaker AI quotas, 1 G5 situations (g5.12xlarge) for coaching job utilization
2. (Non-compulsory) You’ll be able to create an Amazon SageMaker Studio area (check with Use fast setup for Amazon SageMaker AI) to entry Jupyter notebooks with the previous position. (You need to use JupyterLab in your native setup, too.)
   • Create an AWS Identification and Entry Administration (IAM) position with managed insurance policies AmazonSageMakerFullAccess, AmazonS3FullAccess, and AmazonBedrockFullAccess to present required entry to SageMaker AI and Amazon Bedrock to run the examples.
   • Assign as belief relationship to your IAM position the next coverage:

{
    "Model": "2012-10-17",
    "Assertion": [
        {
            "Sid": "",
            "Effect": "Allow",
            "Principal": {
                "Service": [
                    "bedrock.amazonaws.com",
                    "sagemaker.amazonaws.com"
                ]
            },
            "Motion": "sts:AssumeRole"
        }
    ]
}

3. Clone the GitHub repository with the property for this deployment. This repository consists of a pocket book that references coaching property:

git clone https://github.com/aws-samples/amazon-nova-samples.git
cd customization/SageMakerTrainingJobs/Amazon-Nova-LLM-As-A-Choose/

Subsequent, run the pocket book Nova Amazon-Nova-LLM-as-a-Choose-Sagemaker-AI.ipynb to begin utilizing the Amazon Nova LLM-as-a-Choose implementation on Amazon SageMaker AI.

Mannequin setup

To conduct an Amazon Nova LLM-as-a-Choose analysis, it is advisable generate outputs from the candidate fashions you wish to evaluate. On this venture, we used two totally different approaches: deploying a Qwen2.5 1.5B mannequin on Amazon SageMaker and invoking Anthropic’s Claude 3.7 Sonnet mannequin in Amazon Bedrock. First, we deployed Qwen2.5 1.5B, an open-weight multilingual language mannequin, on a devoted SageMaker endpoint. This was achieved by utilizing the HuggingFaceModel deployment interface. To deploy the Qwen2.5 1.5B mannequin, we supplied a handy script so that you can invoke:python3 deploy_sm_model.py

When it’s deployed, inference might be carried out utilizing a helper perform wrapping the SageMaker predictor API:

# Initialize the predictor as soon as
predictor = HuggingFacePredictor(endpoint_name="qwen25-")
def generate_with_qwen25(immediate: str, max_tokens: int = 500, temperature: float = 0.9) -> str:
    """
    Sends a immediate to the deployed Qwen2.5 mannequin on SageMaker and returns the generated response.
    Args:
        immediate (str): The enter immediate/query to ship to the mannequin.
        max_tokens (int): Most variety of tokens to generate.
        temperature (float): Sampling temperature for era.
    Returns:
        str: The model-generated textual content.
    """
    response = predictor.predict({
        "inputs": immediate,
        "parameters": {
            "max_new_tokens": max_tokens,
            "temperature": temperature
        }
    })
    return response[0]["generated_text"]
reply = generate_with_qwen25("What's the Grotto at Notre Dame?")
print(reply)

In parallel, we built-in Anthropic’s Claude 3.7 Sonnet mannequin in Amazon Bedrock. Amazon Bedrock offers a managed API layer for accessing proprietary basis fashions (FMs) with out managing infrastructure. The Claude era perform used the bedrock-runtime AWS SDK for Python (Boto3) shopper, which accepted a person immediate and returned the mannequin’s textual content completion:

# Initialize Bedrock shopper as soon as
bedrock = boto3.shopper("bedrock-runtime", region_name="us-east-1")
# (Claude 3.7 Sonnet) mannequin ID by way of Bedrock
MODEL_ID = "us.anthropic.claude-3-7-sonnet-20250219-v1:0"
def generate_with_claude4(immediate: str, max_tokens: int = 512, temperature: float = 0.7, top_p: float = 0.9) -> str:
    """
    Sends a immediate to the Claude 4-tier mannequin by way of Amazon Bedrock and returns the generated response.
    Args:
        immediate (str): The person message or enter immediate.
        max_tokens (int): Most variety of tokens to generate.
        temperature (float): Sampling temperature for era.
        top_p (float): Prime-p nucleus sampling.
    Returns:
        str: The textual content content material generated by Claude.
    """
    payload = {
        "anthropic_version": "bedrock-2023-05-31",
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p
    }
    response = bedrock.invoke_model(
        modelId=MODEL_ID,
        physique=json.dumps(payload),
        contentType="utility/json",
        settle for="utility/json"
    )
    response_body = json.hundreds(response['body'].learn())
    return response_body["content"][0]["text"]
reply = generate_with_claude4("What's the Grotto at Notre Dame?")
print(reply)

When you may have each features generated and examined, you may transfer on to creating the analysis knowledge for the Nova LLM-as-a-Choose.

Put together the dataset

To create a practical analysis dataset for evaluating the Qwen and Claude fashions, we used the Stanford Query Answering Dataset (SQuAD), a broadly adopted benchmark in pure language understanding distributed below the CC BY-SA 4.0 license. SQuAD consists of 1000’s of crowd-sourced question-answer pairs protecting a various vary of Wikipedia articles. By sampling from this dataset, we made certain that our analysis prompts mirrored high-quality, factual question-answering duties consultant of real-world purposes.

We started by loading a small subset of examples to maintain the workflow quick and reproducible. Particularly, we used the Hugging Face datasets library to obtain and cargo the primary 20 examples from the SQuAD coaching break up:

from datasets import load_dataset
squad = load_dataset("squad", break up="prepare[:20]")

This command retrieves a slice of the total dataset, containing 20 entries with structured fields together with context, query, and solutions. To confirm the contents and examine an instance, we printed out a pattern query and its floor reality reply:

print(squad[3]["question"])
print(squad[3]["answers"]["text"][0])

For the analysis set, we chosen the primary six questions from this subset:

questions = [squad[i]["question"] for i in vary(6)]

Generate the Amazon Nova LLM-as-a-Choose analysis dataset

After making ready a set of analysis questions from SQuAD, we generated outputs from each fashions and assembled them right into a structured dataset for use by the Amazon Nova LLM-as-a-Choose workflow. This dataset serves because the core enter for SageMaker AI analysis recipes. To do that, we iterated over every query immediate and invoked the 2 era features outlined earlier:

• generate_with_qwen25() for completions from the Qwen2.5 mannequin deployed on SageMaker
• generate_with_claude() for completions from Anthropic’s Claude 3.7 Sonnet in Amazon Bedrock

For every immediate, the workflow tried to generate a response from every mannequin. If a era name failed as a result of an API error, timeout, or different concern, the system captured the exception and saved a transparent error message indicating the failure. This made certain that the analysis course of may proceed gracefully even within the presence of transient errors:

import json
output_path = "llm_judge.jsonl"
with open(output_path, "w") as f:
    for q in questions:
        strive:
            response_a = generate_with_qwen25(q)
        besides Exception as e:
            response_a = f"[Qwen2.5 generation failed: {e}]"
        
        strive:
            response_b = generate_with_claude4(q)
        besides Exception as e:
            response_b = f"[Claude 3.7 generation failed: {e}]"
        row = {
            "immediate": q,
            "response_A": response_a,
            "response_B": response_b
        }
        f.write(json.dumps(row) + "n")
print(f"JSONL file created at: {output_path}")

This workflow produced a JSON Strains file named llm_judge.jsonl. Every line comprises a single analysis report structured as follows:

{
  "immediate": "What's the capital of France?",
  "response_A": "The capital of France is Paris.",
  "response_B": "Paris is the capital metropolis of France."
}

Then, add this llm_judge.jsonl to an S3 bucket that you just’ve predefined:

upload_to_s3(
    "llm_judge.jsonl",
    "s3:///datasets/byo-datasets-dev/custom-llm-judge/llm_judge.jsonl"
)

Launching the Nova LLM-as-a-Choose analysis job

After making ready the dataset and creating the analysis recipe, the ultimate step is to launch the SageMaker coaching job that performs the Amazon Nova LLM-as-a-Choose analysis. On this workflow, the coaching job acts as a completely managed, self-contained course of that hundreds the mannequin, processes the dataset, and generates analysis metrics in your designated Amazon S3 location.

We use the PyTorch estimator class from the SageMaker Python SDK to encapsulate the configuration for the analysis run. The estimator defines the compute sources, the container picture, the analysis recipe, and the output paths for storing outcomes:

estimator = PyTorch(
    output_path=output_s3_uri,
    base_job_name=job_name,
    position=position,
    instance_type=instance_type,
    training_recipe=recipe_path,
    sagemaker_session=sagemaker_session,
    image_uri=image_uri,
    disable_profiler=True,
    debugger_hook_config=False,
)

When the estimator is configured, you provoke the analysis job utilizing the match() technique. This name submits the job to the SageMaker management aircraft, provisions the compute cluster, and begins processing the analysis dataset:

estimator.match(inputs={"prepare": evalInput})

Outcomes from the Amazon Nova LLM-as-a-Choose analysis job

The next graphic illustrates the outcomes of the Amazon Nova LLM-as-a-Choose analysis job.

To assist practitioners shortly interpret the result of a Nova LLM-as-a-Choose analysis, we created a comfort perform that produces a single, complete visualization summarizing key metrics. This perform, plot_nova_judge_results, makes use of Matplotlib and Seaborn to render a picture with six panels, every highlighting a unique perspective of the analysis final result.

This perform takes the analysis metrics dictionary—produced when the analysis job is full—and generates the next visible elements:

• Rating distribution bar chart – Exhibits what number of instances Mannequin A was most well-liked, what number of instances Mannequin B was most well-liked, what number of ties occurred, and the way typically the choose failed to supply a choice (inference errors). This offers a direct sense of how decisive the analysis was and whether or not both mannequin is dominating.
• Win price with 95% confidence interval – Plots Mannequin B’s general win price in opposition to Mannequin A, together with an error bar reflecting the decrease and higher bounds of the 95% confidence interval. A vertical reference line at 50% marks the purpose of no choice. If the boldness interval doesn’t cross this line, you may conclude the result’s statistically vital.
• Choice pie chart – Visually shows the proportion of instances Mannequin A, Mannequin B, or neither was most well-liked. This helps shortly perceive choice distribution among the many legitimate judgments.
• A vs. B rating comparability bar chart – Compares the uncooked counts of preferences for every mannequin aspect by aspect. A transparent label annotates the margin of distinction to emphasise which mannequin had extra wins.
• Win price gauge – Depicts the win price as a semicircular gauge with a needle pointing to Mannequin B’s efficiency relative to the theoretical 0–100% vary. This intuitive visualization helps nontechnical stakeholders perceive the win price at a look.
• Abstract statistics desk – Compiles numerical metrics—together with whole evaluations, error counts, win price, and confidence intervals—right into a compact, clear desk. This makes it easy to reference the precise numeric values behind the plots.

As a result of the perform outputs a typical Matplotlib determine, you may shortly save the picture, show it in Jupyter notebooks, or embed it in different documentation.

Clear up

Full the next steps to scrub up your sources:

1. Delete your Qwen 2.5 1.5B Endpoint

   import boto3
   
   # Create a low-level SageMaker service shopper.
   
   sagemaker_client = boto3.shopper('sagemaker', region_name=)
   
   # Delete endpoint
   
   sagemaker_client.delete_endpoint(EndpointName=endpoint_name)

2. Should you’re utilizing a SageMaker Studio JupyterLab pocket book, shut down the JupyterLab pocket book occasion.

How you should utilize this analysis framework

The Amazon Nova LLM-as-a-Choose workflow provides a dependable, repeatable means to match two language fashions by yourself knowledge. You’ll be able to combine this into mannequin choice pipelines to determine which model performs greatest, or you may schedule it as a part of steady analysis to catch regressions over time.

For groups constructing agentic or domain-specific techniques, this method offers richer perception than automated metrics alone. As a result of the whole course of runs on SageMaker coaching jobs, it scales shortly and produces clear visible stories that may be shared with stakeholders.

Conclusion

This submit demonstrates how Nova LLM-as-a-Choose—a specialised analysis mannequin out there by way of Amazon SageMaker AI—can be utilized to systematically measure the relative efficiency of generative AI techniques. The walkthrough reveals the way to put together analysis datasets, launch SageMaker AI coaching jobs with Nova LLM-as-a-Choose recipes, and interpret the ensuing metrics, together with win charges and choice distributions. The absolutely managed SageMaker AI resolution simplifies this course of, so you may run scalable, repeatable mannequin evaluations that align with human preferences.

We suggest beginning your LLM analysis journey by exploring the official Amazon Nova documentation and examples. The AWS AI/ML group provides in depth sources, together with workshops and technical steering, to assist your implementation journey.

To study extra, go to:

In regards to the authors

Surya Kari is a Senior Generative AI Knowledge Scientist at AWS, specializing in growing options leveraging state-of-the-art basis fashions. He has in depth expertise working with superior language fashions together with DeepSeek-R1, the Llama household, and Qwen, specializing in their fine-tuning and optimization. His experience extends to implementing environment friendly coaching pipelines and deployment methods utilizing AWS SageMaker. He collaborates with clients to design and implement generative AI options, serving to them navigate mannequin choice, fine-tuning approaches, and deployment methods to realize optimum efficiency for his or her particular use circumstances.

Joel Carlson is a Senior Utilized Scientist on the Amazon AGI basis modeling crew. He primarily works on growing novel approaches for bettering the LLM-as-a-Choose functionality of the Nova household of fashions.

Saurabh Sahu is an utilized scientist within the Amazon AGI Basis modeling crew. He obtained his PhD in Electrical Engineering from College of Maryland School Park in 2019. He has a background in multi-modal machine studying engaged on speech recognition, sentiment evaluation and audio/video understanding. At present, his work focuses on growing recipes to enhance the efficiency of LLM-as-a-judge fashions for numerous duties.

Morteza Ziyadi is an Utilized Science Supervisor at Amazon AGI, the place he leads a number of initiatives on post-training recipes and (Multimodal) massive language fashions within the Amazon AGI Basis modeling crew. Earlier than becoming a member of Amazon AGI, he spent 4 years at Microsoft Cloud and AI, the place he led initiatives targeted on growing pure language-to-code era fashions for numerous merchandise. He has additionally served as an adjunct college at Northeastern College. He earned his PhD from the College of Southern California (USC) in 2017 and has since been actively concerned as a workshop organizer, and reviewer for quite a few NLP, Laptop Imaginative and prescient and machine studying conferences.

Pradeep Natarajan is a Senior Principal Scientist in Amazon AGI Basis modeling crew engaged on post-training recipes and Multimodal massive language fashions. He has 20+ years of expertise in growing and launching a number of large-scale machine studying techniques. He has a PhD in Laptop Science from College of Southern California.

Michael Cai is a Software program Engineer on the Amazon AGI Customization Crew supporting the event of analysis options. He obtained his MS in Laptop Science from New York College in 2024. In his spare time he enjoys 3d printing and exploring revolutionary tech.