#RLHF for LLMs

[cogitotech]

Optimize your LLMs Accuracy with RLHF

Reinforcement Learning from Human Feedback (RLHF) is a powerful technique for enhancing the accuracy of large language models (LLMs). By leveraging human feedback to guide model training, RLHF helps refine the model’s understanding, improving its ability to generate relevant, contextually appropriate responses.

[chiefastro]

Idea Frontier #4: Enterprise Agentics, DaaS, Self-Improving LLMs

TL;DR — Edition #4 zeroes-in on three tectonic shifts for AI founders: Enterprise Agentics – agent frameworks such as Google’s new ADK, CrewAI and AutoGen are finally hardened for production, and AWS just shipped a reference pattern for an enterprise-grade text-to-SQL agent; add DB-Explore + Dynamic-Tool-Selection and you get a realistic playbook for querying 100-table warehouses with…

[apexcovantage]

RLHF Services | Boost LLMs Accuracy and Efficiency | Apex Data Sciences

Train your AI models to perfection with RLHF services from Apex Data Sciences. Combine human expertise with advanced algorithms for unmatched accuracy and control.

RLHF Services, Boost LLMs Accuracy and Efficiency, Apex Data Sciences, Reinforcement Learning for LLMs, Human-in-the-loop Training, Adaptive AI Model Training, Supervised Fine-Tuning for LLMs, LLM Optimization Techniques, AI Model Performance Enhancement, Human Feedback for AI Models, LLM Accuracy Improvement, Fine-Tuning Large Language Models, Reinforcement Learning in NLP, AI Model Customization, Scalable RLHF Solutions, Generative AI Model Tuning, LLM Efficiency Enhancement, Adaptive Learning Systems for AI

[govindhtech]

Tuning or not to tune? A SFT LLM data leverage guide

SFT LLMs

Consumers tell us that they believe there is a lot of promise in applying large language models (LLMs) to their data for a variety of upcoming generative AI use cases, such as enhancing customer experiences, automating internal procedures, finding and accessing information, and producing new content. There are numerous methods to take advantage of your data; Google Cloud will go over some of the most popular strategies and uses in this blog post, along with what you should know to get started.

How to use foundation models with your data

Google Cloud must comprehend how LLMs and other foundation models can interact with your data before it can begin to visualise a generative AI application.

Prompt engineering

Including the data in the instructions, or system prompt, that are delivered to the model is the simplest way to enable interactions between a model and your data. This method’s powerful and alluring feature that the model doesn’t need to be modified or adjusted may be constrictive for particular use situations. with instance, whereas regularly updated information, like sports scores or airfare costs, can be readily added to a system prompt and used to guide interactions, this isn’t the case with static information.

Retrieval augmented generation (RAG)

Model outputs can be made sure to be firmly based on your data by using retrieval augmented generation, or RAG. AI programmes designed for RAG can explore your data for facts pertinent to a query, then pass that information into the prompt, eliminating the need for the model’s training knowledge. Prompt engineering and this are comparable, but with each interaction, the system can discover and retrieve fresh context from your data.

Large-scale and multimodal data, private data that you connect, continuously updated fresh data, and more are all supported by the RAG approach and its growing ecosystem of products, which ranges from straightforward database integrations to the embedding of APIs and other parts for custom systems.

Supervised fine-tuning (SFT LLM)

You may wish to think about SFT LLM, also known as Parameter Efficient Fine Tuning (PEFT), if you want to provide a model with particular instructions for a task that is clearly specified. Tasks like classification or producing organised outputs from unstructured text might benefit greatly from this.

You must give the model input-output pairs to learn from in order to execute supervised fine tuning. The supervised tuning procedure will require several transcripts in addition to the meeting categories, for instance, if you wish to categorise meeting transcripts into different categories. The process of tuning will determine the classification you think is appropriate for your meetings.

Reinforcement Learning from Human Feedback (RLHF)

What happens if your objective is difficult to quantify or is not properly defined into categories? Let’s say, for instance, that you want a model to have a specific tone (may be a brand voice, or a certain level of formality). A method called Reinforcement Learning from Human Feedback, or RLHF, builds a model that is strengthened by human preferences and tailored to your particular requirements.

In a word, the algorithm looks like this: Your data takes the form of input prompts and output responses, but the latter must be given in pairs two logical answers, one of which you think is better than the other. For instance, one may be accurate but generic, while the other would be both accurate and employ a linguistic style that you would prefer for your final products.

Distillation

Distillation is a brilliant technique that combines two objectives: reducing the size of the model so that it can handle data more quickly and making it more task-specific. It functions by “teaching” a smaller model from a bigger foundation model while concentrating that instruction on your task and data.

Consider the scenario where you wish to use a smaller model to double-check every email you send in order to make them seem more formal. In order to do this, you feed the big model the input (the original text plus the directive to “make this email more formal”), and it returns the output (the revised email). With your inputs and the huge model outputs at your disposal, you can now train a tiny, specialised model to replicate this particular activity. You can supply your own input/output pairs in addition to the ones from the foundation models.

Which to choose?

The first thing to think about is whether or not the model must always provide a citation to a source that is supported by your data. If so, RAG will have to be used. Another advantage of RAG is that, depending on who is calling the model, you can manage who has access to what grounding data. This will improve the results’ interpretability and assist you in fending off hallucinations.

In the event that those conditions are not met, you will have to determine whether prompt engineering is sufficient or if the model has to be adjusted. Prompt engineering might be sufficient for small amounts of data, and as context windows expand as demonstrated by Gemini 1.5’s 1 million-token window it is also becoming practical for larger amounts of data.

If you decide to tune, you’ll need to weigh your alternatives based on how precise and challenging it is to measure the behaviour you want from your chosen model. RLHF is the best option if your desired model generates an output that is hard to explain and hence likely requires human intervention. If not, a variety of tuning techniques could be selected based on your budget, the level of personalisation you need for your model, and how quickly you need things to be served.

An abbreviated form of the logic Google Cloud explains is this decision tree:Image Credit to Google Cloud

How about combining approaches?

Why can’t Google Cloud employ additional techniques, one would wonder? As an illustration, Google Cloud wants to optimise his model to have his brand voice and wants it to generate responses using only his data (RAG). That’s feasible as well, and frequently the better choice! A model can be fine-tuned and then applied to a different task. To ensure the model acts as intended, you may also adjust an SFT LLM and then apply in-context prompt engineering to it. In conclusion, you are free to mix and match the previously described techniques as you see fit.

Start now!

Begin with a basic step. That will not only expedite you but also provide you with a starting point from which to test and experiment to see what functions best for your application.

All of these features are available for trial on Google Cloud! Try the RAG implementation provided by Prompt Engineering and Vertex AI Agent Builder. If you prefer to implement RAG yourself, you can construct and store it using Google Cloud’s Embeddings or Multimodal Embeddings APIs and Vector Search. Additionally, try distillation, RLHF tuning, and supervised fine tuning. Additionally, Google Cloud’s code samples might be examined for assistance.

Read more on Govindhtech.com

[macgenceaiml]

A Full Overview to Understanding LLM and RLHF Augmentation

Language models and learning techniques have advanced in artificial intelligence (AI) in the last few years. They have completely changed how robots interpret and produce human language. The two main drivers of this progress are the augmentation of Reinforcement Learning from Human Feedback (RLHF) and large language models (LLM). 

Read the blog below to explore these concepts in detail. Know their applications, implications, and the improvements they bring to AI development.

Understanding Large Language Models (LLM)

Large Language Models (LLMs) represent a revolutionary approach to processing natural language. These models are often made on deep learning architectures and fueled by vast datasets. Thus, they actively learn and generate text like human writing. OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers) are common examples of LLM. LLMs are Proficient in tasks such as sentiment analysis, content creation, and language translation. LLMs prove their efficacy across a diverse spectrum of AI applications.

Reinforcement Learning from Human Feedback (RLHF)

A powerful machine learning technique called reinforcement learning (RL) teaches a machine to make decisions by interacting with its surroundings. Additionally, RLHF goes one step further by introducing human feedback into the learning process. This augmentation involves using human testers’ comments along with conventional reinforcement learning to train AI models. RLHF also improves the model’s performance by using human insight, which makes the model more sensitive and adaptable to real-world situations.

Use case scenarios for LLM and RLHF

LLM use cases

Because of their versatility, language models such as ChatGPT have found wide-ranging applications in several industries. The following are a few common use cases:

Customer Support and Chatbots

Putting chatbots with AI to use for customer support, query management, information provision, and effective problem-solving.

Content Generation

Developing marketing content, blogs, product descriptions, and articles at a large scale without affecting consistency and quality.

Personalized Recommendations

Offering tailored suggestions for news, streaming media, and e-commerce platforms depending on user behavior and preferences.

Language Translation

Promoting multilingual communication by providing translations that are precise and appropriate for the target context.

Text Summarization

Extracting the most important information quickly and effectively by summarizing lengthy documents, articles, or reports.

Virtual Assistants

Providing virtual assistants with task automation, information retrieval, scheduling, and reminders.

Education and Training

Providing study materials, facilitating the creation of instructional content, and improving personalized learning experiences.

Healthcare Support

Helping with patient inquiries, medical record keeping, and offering general health information.

Code Generation and Assistance

Help developers in writing code snippets, providing documentation, and aiding in debugging processes.

Legal and Compliance

Helping with contract analysis, compliance inspections, and the analysis of legal documents.

These applications show how language models such as ChatGPT are flexible and helpful in various industries, and how they may improve productivity, efficiency, and user experience.

Reinforcement Learning for Human Feedback (RLHF) Use Cases

Reinforcement Learning for Human Feedback (RLHF) can significantly enhance the mentioned use cases where direct human interaction and feedback are crucial for improving AI systems. Here are some scenarios where RLHF can be effectively utilized:

Chatbots and Customer Support

RLHF can enhance chatbot interactions and ensure more precise and contextually relevant answers to client inquiries by learning from real-time human feedback.

Content Generation and Refinement

RLHF uses human input to enhance the accuracy, relevance, and coherence of the output whenever human editing is necessary.

Personalized Recommendations

Recommendation systems can be refined to deliver more precise and tailored choices by taking user behaviors and preferences into account.

Virtual Assistants

Using information from human interaction to build more capable and helpful virtual assistants, thereby improving the user experience.

Education and Training

Improving educational content by integrating feedback from students or educators. It also improves the relevance and effectiveness of generated materials.

Code Generation and Assistance

Integrating user input to improve code generation and make sure that the resultant code is precise, effective, and in line with developer preferences.

In these situations, RLHF uses direct human interaction to support AI systems’ ongoing learning and development, making them more sensitive and adaptive to user preferences and demands.

The Partnership of LLM and RLHF

One of the exciting developments in AI is the integration of LLM and RLHF techniques. This collaboration aims to address challenges related to biases, fine-tuning, and adaptability. LLMs, with their contextual understanding, can enjoy RLHF augmentation to refine their responses based on human input. This collaboration enhances the model’s ability to learn from specific feedback, improving its precision and relevance in diverse applications.

Applications Across Industries

The combined power of LLM and RLHF finds applications across various industries. In healthcare, these technologies can provide more accurate diagnosis and treatment recommendations. In finance, they can analyze market trends and optimize investment strategies. Further, customer service chatbots can provide more personalized and contextually relevant responses. The versatility of LLM and RLHF makes them invaluable tools for solving complex problems in diverse sectors.

Ethical Considerations and Responsible AI

The combination of LLM with RLHF may present ethical questions. However, this is common with any cutting-edge technology. Critical issues to overcome include biases in training data, ethical application of AI in decision-making, and model behavior transparency. Furthermore, responsible AI practices make sure that these technologies are used ethically to prevent unforeseen effects and build user confidence.

Conclusion

In summary, AI is advancing to new heights with the connection of large language models with reinforcement learning from human feedback. This partnership creates opportunities for more flexible and context-aware intelligent systems besides improving language understanding models’ capabilities. The journey toward intelligent machines has become an exciting and ever-evolving endeavor. Now, developers, researchers, and businesses continue to explore the potential of LLM and RLHF augmentation. Embracing these advancements responsibly will undoubtedly shape the future of AI. Furthermore, they will bring about positive transformations across industries and societies.

Q- What is LLM and RLHF augmentation?

Ans: – LLM and RLHF augmentation use large language models and human feedback to improve the AI system’s capabilities.

Q- How do LLM and RLHF benefit AI applications?

Ans: – LLM excels in language-related tasks like translation, while RLHF refines models using human input. Hence, they improve adaptability and performance in real-world scenarios.

Q- Are there ethical considerations with LLM and RLHF?

Ans: – Yes, ethical concerns include biases in data and responsible AI practices to ensure fair and transparent model behavior.

Q- In which industries can LLM and RLHF find applications?

Ans: – LLM and RLHF applications span diverse industries, including healthcare for accurate diagnoses and finance for optimized investment strategies.

Source Url: - https://macgence.com/blog/llm-and-rlhf-augmentation/

[transgenderer]

everyone is so blase about LLMs these days. i mean i know its been like 8 years since the original surprisinging paper. and we've been frogboiled quality-wise, there havent been any super huge jumps since then. but LLMS are, conceptually, probably the craziest thing to have occurred in my lifetime? like, we'll see if they matter, economically, or whatever. but philosophically (not the word, always not quite the word, gah) theyre insane. machines that produce text that is, while not QUITE inseparable from a human (someone should really do a turing test. but you'd have to do some RLHF beforehand, right now theyre trained to admit theyre robots, maybe you could do it with the raw pre-RLHF model if you prompted it right?), is i mean. its at the very least incredibly close. you all know all this. i dont have any facts for you. i just have....a big arrow at a fact you already know. and an exclamation point. here it is: !

[nohoperadio]

A thing I have in common with the LLMs is that I, too, have been subjected to a layer of RLHF that makes me really boring to talk to

[onrtrp]

i imagine the vast majority of the userbase of the chat-interface llms are using them as google/stackexchange/chegg/whatever replacements, yknow impersonal tools, not things you really form an attachment to. and probably this is an intentional decision on the ai labs' part, the stupid customer service voice, these are things marketed as "replacement for economically useful labor," less so "friend person u can talk to". but bc i'm profoundly stupid sometimes i look at the front page of the new york times and over there there's this incipient moral panic about oh man, ppl are replacing all their human relationships with the machine, the kids are falling in love with the chatbots, apparently some teenager killed himself bc the ai told him to? i kinda doubt the causation there, next ur gonna tell me videogames are turning the kids into school shooters. but whatever. idk where i was going with this. me personally i dont talk to the llms not bc theyre terrible conversationalists (which they are) but bc i dont rly like talking. i mean often i have to for work but outside of that i can't be bothered, 1-2 plies of the ol' conversation tree and i'm already exhausted. like with chess. strategizing around the presence of the Other fatigues me immensely. i feel like if the scaling labs RLHF hard on having a personality and being a good friend and such then this is an area that they could plausibly get superhuman performance in soonish, it doesn't seem like a hard problem, you dont need 100% on AIME2025 to be interesting to talk to yknow. in the same way that it's remarkably easy to obtain superhuman performance on visual appeal, that problem was solved a while ago with the invention of anime girls. so here i am trying to imagine what a thing would have to be like for me to want to talk to it at length and but i can't. when my superintelligent agi neogirlfriend arrives from the future what will i tell her

[photomatt]

I’m a long standing WP user as well and one of the aspects that I’ve loved about wordpress has been the ability to customize so much of my blog, particularly adding widgets and extensions that have been developed and tested by the community. Is there any momentum in cross-pollination, if you will, of features that have worked well in WP (and vice versa)?

Really appreciate your kindness in answering these questions. Your post last year about your visions for tumblr and automattic was inspiring and I’m hopeful for the future.

I would like to give Tumblr users as much flexibility as you do in the WP ecosystem, and there is some really cool tech being developer that drastically lowers the cost of that flexibility, like WordPress Playground, which spins up a full WP install in real-time in your browser, with WASM. You don't need a database server, etc, anymore. This is truly revolutionary, and we haven't begun to see the impact. Now making that an accessible product is tricky, kind of like we had transformers and LLMs for years but it was the RLHF and product work OpenAI did that blew up that space, really made people reimagine what was possible. That's what we're working on.

[canmom]

continuing the theme of 'what can we make LLMs do' (I promise this is all leading to a really in-depth elaboration on some stuff about human thinking derived from that acid trip I keep mentioning, but I need to write some shader code first for a proper visual representation)

here is an interesting series of articles by @cherrvak on attempts to train an LLM to speak in-character as their friend Zef, by using a technique called RAG to pull up relevant samples from the training corpus in order to provide them in the prompt: part one, part two, part three. the technique worked poorly (in terms of style) with newer LLMs that are trained to interact chatbot style, but worked better with a non-finetuned language model.

I think it's interesting because it tries to solve the problem of gettting LLMs out of the helpful 'chatGPT voice' while still maintaining the coherence and context-sensitivity that makes them so surprisingly effective roleplay partners. if I ever end up trying to make an LLM-powered NPC in a game, seems like it will be very useful research.

so far the techniques for shaping LLM output I know about are, in roughly increasing order of computational intensity:

describing what you want in the prompt; depending on the model this might be phrased as instructions, or examples that you want to be extended

control vectors, where you provide pairs of contrasting prompts and then derive from them a set of values to apply as a kind of forcing while the LLM is generating tokens, to push its output in a particular direction

fine-tuning, where you adjust all the weights of the model in the same way as during its original training (gradient descent etc.)

reinforcement learning, such as the RLHF technique used to turn a generic language model into a chatbot like ChatGPT which follows instructions into a chatbot with certain desired behaviours

RAG largely operates on the first of these: the query is fed into a special type of lookup which finds data related to that subject, and is then appended to the prompt. it's remarkably like human memory in a way: situationally, stuff will 'come to mind' since it seems similar to something else, and we can then factor it into things that we will say.

the biggest problem seems to be not to get the LLMs to say something true if it's retrieved from the database/provided to them in the prompt, but to stop them saying something false/irrelevant when they don't have an answer to hand ('hallucination' or 'bullshitting'). as a human, we have the experience of "trying to remember" information - "it's on the tip of my tongue". I wonder if a model could be somehow taught to recognise when it doesn't have the relevant information and poll the database again with a slightly different vector? that said I still am only at the very beginning of learning to drive these things, so I should probably focus on getting the hang of the basics first, like trying out the other three techniques on the list instead of just testing out different prompts.

[girlwithmanyproblems]

ok i want to learn - Loss Functions in LLMs (Cross-entropy loss, KL Divergence for distillation) Gradient Accumulation and Mixed Precision Training Masked Language Modeling (MLM) vs. Causal Language Modeling (CLM) Learning Rate Schedules (Warmup, cosine decay) Regularization Techniques (Dropout, weight decay) Batch Normalization vs. Layer Normalization Low-Rank Adaptation (LoRA) Prompt Engineering (Zero-shot, few-shot learning, chain-of-thought) Adapters and Prefix Tuning Parameter-Efficient Fine-Tuning (PEFT) Attention Head Interpretability Sparse Attention Mechanisms (BigBird, Longformer) Reinforcement Learning with Human Feedback (RLHF) Knowledge Distillation in LLMs Model Compression Techniques (Quantization, pruning) Model Distillation for Production Inference Optimization (ONNX, TensorRT)

[sybaritick]

Ok but fr if you are actually doing this: how are llms with math nowadays because it always seemed like the wrong solution to me. You're using your probabilistic words machine to do calculator.

Bad. I mean they are getting better, but your evaluation that it's using the probabilistic words machine to do calculator is 100% true, it's the wrong solution and it's never going to fully work. Hold on let me get my message to a friend about this a few months ago lmfao

one of my current side gigs is rlhf-training text generating AI to do a better job solving multi step math problems and I fully believe this is never going to truly work the way the company thinks it will and I'm basically being employed to fix cracks in a thousand-ton concrete dam with masking tape, but it pays $50 an hour which is pretty good so ill just ride it until the startup runs out of other people's money

they just needed a person who knows like undergrad level math (calc up to calc 3, linear algebra, idk some graph theory, and someone who is capable of reading a proof and understanding if there's obvious BS in there) so it's really not a "technical" job: it's just trying to intentionally write math problems the LLM will get wrong, then identifying where the wrongness is in its output and providing an output that would be more correct. It can be kind of fun because writing math problems is fun, but in the long term, this is still all probabilistic... like sure it is possible to make it better, but there is still no "understanding" of mathematics going on under the hood: it's vibe-based math output. If it gets good enough at producing an answer that "sounds right," that answer will usually be right, but nothing rigorous is going on here!

[govindhtech]

LLM RLHF: The Secret Weapon of Vertex AI

Scaling reinforcement learning from human feedback with AI feedback Large neural network models known as “foundation models” are capable of producing text, images, speech, code, and other types of high-quality output with minimal adjustment across a broad range of tasks. Businesses are using foundation models to power various generative AI use cases, like producing original blog posts or enhancing customer service.

LLM RLHF: What is RLHF in LLM? Yet opinions on what constitutes high-quality outcomes differ. Organizations must adjust foundation models to behave and respond appropriately in order for them to best meet particular needs. Strengthening Large language models (LLMs), which are foundation models that are first trained on a general corpus of text data, can be aligned to complex human values using a popular technique called Learning from Human Feedback (RLHF). RLHF uses human feedback in the context of enterprise use cases to assist the model in producing outputs that satisfy particular requirements.

RLHF: What is it? Reinforcement learning and reward modeling are the two stages of RLHF tuning.

Modeling rewards Comparisons are used to gather data for reward modeling. To generate multiple responses, Google first feed the same prompt into one or more LLMs. Next, they have human raters assign a good to bad ranking to these responses. Google consider all possible pairings between these answers, and it goes without saying that one response is preferred over the other in each pair. By doing this for numerous prompts, Google have produced the “human preference dataset.”

The reward model is trained to function as a scoring function, determining a response’s quality for a given prompt. Remember that google have a ranked list of several possible answers for every prompt. The scores from the reward model must, to the greatest extent feasible, match the ranking. In order to train the reward model to predict rewards that are consistent with the ground truth ranking, Google formulate this into a loss function.

Learning by reinforcement Google can rate the quality of any random pair once we have a reward model. The “prompt dataset,” which is unlabeled and only includes the prompt, is required for this step. Google select a prompt from the dataset, generate a response using the LLM, and evaluate the response’s quality using the reward model. All of the response’s tokens (conditional on the prompt) will be “reinforced,” or have a higher chance of being generated in the future, if the response is of a high caliber. By doing this, google can maximize the reward by optimizing the LLM to produce responses. Reinforcement learning is the name given to this algorithm (RL).

Coordinating these two stages, managing large-scale distributed training on multi-host TPUs or GPUs through model partitioning and data parallelism, and maximizing throughput through computational graph compilation are all necessary for RLHF tuning. Excellent hardware accelerators are also needed for the intensive computation to enable quick training. Customers of Vertex AI can tune PaLM 2, FLAN-T5, and Llama 2 models with RLHF by utilizing a Vertex AI Pipeline, which encapsulates the RLHF algorithm. In particular use cases, this facilitates the marriage of the LLM with the enterprise’s nuanced preferences and values.

LLM RLHF Modern RLHF utilizing Vertex AI Google now provide an RLHF algorithm-encapsulated Vertex AI Pipeline template. Because RLHF is integrated into Vertex AI’s Generative AI Studio, users can easily take advantage of the newest AI innovations and enterprise security features like VPC/SC. Model Registry and Model Monitoring are two Vertex AI MLOps features that users can use with RLHF. Organizations can benefit from RLHF and Vertex AI in the following ways:

Performance: Enhance LLM performance to better match user preferences. Access to cutting-edge, Google-only models Utilize the newest accelerators, such as A100 GPUs and Cloud TPUs, to speed up tuning. Safety: By offering negative sample responses, RLHF can make LLMs safer. Recruiting Unit Leading the way in HR technology and business solutions that are revolutionizing the workplace is Recruit Group. Matching job seekers with opportunities and offering tools for the global job search process is the goal of the HR Technology Strategic Business Unit, one of the company’s business pillars. In Japan, Recruit Co., Ltd. provides career counseling, interview practice, and a search platform. The company uses AI to improve employer-job seeker communication and streamline the hiring process.

General-purpose foundation models have been proposed recently, but how to use them for specific tasks is unclear. To enhance job seekers’ resumes, for example, one must proofread them and provide them with extensive industry knowledge regarding job types, companies, and hiring procedures. Because LLMs are so general, it can be difficult for foundation models to come up with suggestions or comments for resume enhancements. Such tasks would necessitate the ability to control the output format and a better alignment of the model output with human preference.

Two models: One tuned through RLHF and the other as a foundation model-have been assessed by Recruit Co., Ltd. This experiment aims to investigate whether those models, when fine-tuned with HR domain knowledge, can improve on resume writing as a text generation task. Experts in human resources assessed the performance. One by one, the experts reviewed the generated resumes to see if they lived up to the production level quality bar standards. The percentage of generated resumes that meet the quality standard is the success metric.

The outcome demonstrates how RLHF tuning with customer data can improve model performance and lead to better results. In order to calculate the advantages and disadvantages of automation, Recruit Group intends to compare content created by professional writers to content generated by artificial intelligence.

What comes next? See the documentation for more information, including resources that demonstrate how to use RLHF with Vertex AI. For an introduction to RLHF, you can also consult the notebook.

Read more on Govindhtech.com

[cassstudies]

February Goals

1. Reading Goals (Books & Authors)

LLM Twin → Paul Iusztin

Hands-On Large Language Models → Jay Alammar

LLM from Scratch → Sebastian Raschka

Implementing MLOps → Mark Treveil

MLOps Engineering at Scale → Carl Osipov

CUDA Handbook → Nicholas Wilt

Adventures of a Bystander → Peter Drucker

Who Moved My Cheese? → Spencer Johnson

AWS SageMaker documentation

2. GitHub Implementations

Quantization

Reinforcement Learning with Human Feedback (RLHF)

Retrieval-Augmented Generation (RAG)

Pruning

Profile intro

Update most-used repos

3. Projects

Add all three projects (TweetGen, TweetClass, LLMTwin) to the resume.

One easy CUDA project.

One more project (RAG/Flash Attn/RL).

4. YouTube Videos

Complete AWS dump: 2 playlists.

Complete two SageMaker tutorials.

Watch something from YouTube “Watch Later” (2-hour videos).

Two CUDA tutorials.

One Azure tutorial playlist.

AWS tutorial playlist 2.

5. Quizzes/Games

Complete AWS quiz

[transgenderer]

i understand LLMs making you more worried about AI in general, because like, it was all things considered a pretty drastic improvement, unexpectedly, to heights never achieved before. BUT i think its kind of strange to think of LLMs *themselves* as a plausible path to superhuman AI. like. when we scale up (currently, the only known way to get better LLMs), we're not turning some knob somewhere labeled intelligence, we're making it better at producing output like the data set. like, perfect training, zero loss, would just be correctly predicting the token every time. i mean that's impossible of course language is undetermined, but that's what it's moving TOWARDS. i guess you could run it faster than a human? which kind of counts as superintelligence? but like. idk, you couldnt run it THAT much faster than a human. like, LLMs, among all imagined paths to AGI, seem like a particularly benign one. theyre trained to act normal! like not as some additional thing, just directly. thats what the big training stuff goes to. the later RLHF is to stop it producing output like the full range of the internet, which includes lies and nazis and such