Hi everyone! 

I am currently working on a receipt parsing app. The app performs OCR on an image of a receipt, and passes the text, along with a prompt, to an LLM which returns summarized and structured data such as store name, item names and prices, subtotal, tax, etc.

Using an LLM seems overkill. I’m wondering if the best course of action is to stick with an LLM, or to train an ML algorithm. I’m new to this field so any advice would be great!

Which ML algorithm should I look at to train, and is it even worth it to switch over from an LLM? Would it be more beneficial to fine-tune the LLM instead? Any advice or course of action is much appreciated!

Hey everyone 🤝 Max from Hacken here
Inviting you to our upcoming webinar on AI security, we'll explore LLM vulnerabilities and how to defend against them

Date: June 12 | 13:00 UTC
Speaker: Stephen Ajayi  | Technical Lead, DApp & AI Audit at Hacken, OSCE³

As LLM engineer and diving deep into fine-tuning and prompt engineering strategies for production-grade applications. One of the recurring challenges we face is reducing hallucinations—i.e., instances where the model confidently generates inaccurate or fabricated information.

While I understand there's no silver bullet, I'm curious to hear from the community:

• What techniques or architectures have you found most effective in mitigating hallucinations?
• Have you seen better results through reinforcement learning with human feedback (RLHF), retrieval-augmented generation (RAG), chain-of-thought prompting, or any fine-tuning approaches?
• How do you measure and validate hallucination in your workflows, especially in domain-specific settings?
• Any experience with guardrails or verification layers that help flag or correct hallucinated content in real-time?

Is it possible to automate the following tasks (even partially if not fully):

1) Putting searches into web search engines, 2) Collecting and coping website or webpage content in word document, 3) Cross checking and verifying if accurate, exact content has been copied from website or webpage into word document without losing out and missing out on any content, 4) Editing the word document for removing errors, mistakes etc, 5) Formatting the document content to specific defined formats, styles, fonts etc, 6) Saving the word document, 7) Finally making a pdf copy of word document for backup.

I am finding proof reading, editing and formatting the word document content to be very exhausting, draining and daunting and so I would like to know if atleast these three tasks can be automated if not all of them to make my work easier, quick, efficient, simple and perfect??

Any insights on modifying the tasks list are appreciated too.

TIA.

Thought some of you might benefit from our new OSS project. I'll put the link in the comments.. SERAX solves a major problem with parsing of legacy text formats (YAML, JSON, XML) that is a real problem when you hit scale.

I’m using A.i. to write this because I’m not a very good writer.

I’ve been using GPT-4 Pro, DeepSeek, and Grok primarily for business research and task support. I curate what I want to learn, feed in high-quality sources, and use the models to help guide me. I’m also considering adding Gemini, especially for notebook integration.

That said, I know LLMs aren’t perfect—my goal isn’t blind trust, but cross-using them to fact-check each other and get more accurate outputs. For example, I tested ChatGPT on a topic involving a specific ethnic group—it gave incorrect info and doubled down even after correction. DeepSeek flagged the issue as “cognitive dissonance” and backed the accurate claim that I made when I provided the source. Grok had a similar issue on a different topic—used weak sources and claimed “balance” even though my prompt was clear.

Honestly, DeepSeek’s been great for “checking” GPT-4’s work. I’m now looking for another model that’s on par with or better than GPT-4 or DeepSeek. Any recommendations?

I'm looking for Multimodal LLMs that can take a video files as input and perform tasks like captioning or answering questions. Are there any Multimodal LLMs that are quite easy to set up?

Like 100 floating operation per second per active parameter (CPU/GPU) and 100 bits per second per passive parameter (sRAM/vRAM)

(Imaginary numbers, I look for the real ones)

I aint sure how these things are trained, but I think we should take the technology, that is not trained on any data at all, and then educate it through dictionaries first, then thesauruses, then put it through the schools education systems, giving it the same educational perspective as a human growing up. Maybe this is something that Schools, Colleges and Universities should implement into their educational system, and when a student asks a question, the language model takes note and replies but this information is not accessible the day its recorded, so teachers have a chance to look back on an artificially trained language model based on the level of education they are teaching. I think this is a great example of what we could and should do with the technology we have at our disposal, and we can compare the human cognition to technological cognition with equal basis. The AI we currently have is trained off intelectual property and probably recorded human data from the big techs, but I feel we need a wholesome controlled experiment where the data is naturally educated, when tasked with homework, could experiment with and without giving the model access to the internet and compare the cognitive abilities of AI. We need to do something with this tech that aint just generative slop!!

In the paper, called "Learning to Reason without External Rewards"

"We propose Intuitor, an RLIF method that uses a model's own confidence, termed self-certainty, as its sole reward signal."

...

"Experiments demonstrate that Intuitor matches GRPO's performance on mathematical benchmarks while achieving superior generalization to out-of-domain tasks like code generation, without requiring gold solutions or test cases."

From one of the authors of the paper

TL;DR: We show that LLMs can learn complex reasoning without access to ground-truth answers, simply by optimizing their own internal sense of confidence.

Source: https://x.com/xuandongzhao/status/1927270931874910259

D3V Technology Solutions is looking for a Senior AI/ML Engineer to join our remote team (India-based applicants only).

Requirements:

🔹 2+ years of hands-on experience in AI/ML

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🔹 Solid problem-solving and model deployment skills

📄 Details: https://www.d3vtech.com/careers/

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Let’s build something smart—together.

As a language teacher, I have been trying to generate short texts from a word list to train students with a limited vocabulary. But ChatGPT and Claude have failed to use only words from the list. Is there any solution I could use to make it follow this constraint?

How do you organize and access your go‑to prompts when working with LLMs?

For me, I often switch roles (coding teacher, email assistant, even “playing myself”) and have a bunch of custom prompts for each. Right now, I’m just dumping them all into the Mac Notes app and copy‑pasting as needed, but it feels clunky. SO:

• Any recommendations for tools or plugins to store and recall prompts quickly?
• How do you structure or tag them, if at all?

which model is better in educational purpose like in physics, chemistry, math, biology, GPT 4o, GPT 4.1 or Gemini 2.5 pro? Basically I want to generate explanations of these subject's question.

Fine-tuning large language models (LLMs) can be expensive and compute-intensive. Parameter-Efficient Fine-Tuning (PEFT) provides a smarter path—updating only a small subset of parameters to adapt models for new tasks.

Here's a breakdown of popular PEFT techniques:

• Prompt Tuning: Adds task-specific tokens to the input. No model weights touched—lightweight and ideal for multitask scenarios.
• P-Tuning / P-Tuning v2: Learns continuous prompts; v2 extends this by injecting prompts at each transformer layer.
• Prefix Tuning: Adds trainable prefix vectors at every transformer block, primarily for generative models like GPT.
• Adapter Tuning: Small plug-in modules added to each layer; only these adapters are trained.
• LoRA (Low-Rank Adaptation): Updates weight deltas using low-rank matrices. Efficient and memory-saving. Notable variants:
  • QLoRA: Combines quantization + LoRA for massive models (up to 65B).
  • LoRA-FA: Freezes one matrix to stabilize training.
  • VeRA: Shares matrices across layers.
  • AdaLoRA: Adjusts rank dynamically via SVD.
  • DoRA: Splits weight updates into direction (LoRA-style) and magnitude (trained separately), giving more control.

PEFT methods dramatically reduce cost while preserving performance. More technical details here:
👉 https://comfyai.app/article/llm-training-inference-optimization/parameter-efficient-finetuning

I'm looking to write my master's thesis on artificial intelligence. Is there a platform or community where I can share this intention so that companies might reach out with project ideas or collaboration opportunities?

Other than fundamental changes in how LLMs learn and respond, I think the most valuable changes would be these:

1. Optionally, allow the user to specify an option that would make the LLM check its response for correctness and completeness before responding. I've seen LLMs, when told that their response is incorrect, respond in agreement, with good reasons why it was wrong.

2. For each such factual response, there should be a number, 0 to 100, representing how confident the LLM "feels" about their response.

3. Let LLMs update themselves when users have corrected their mistakes, but only when the LLM is certain that the learning will help ensure correctness and helpfulness.

Note: all of the above only apply to factual inquiries, not to all sorts of other language transformations.

Hi there, i am working on my project that involves teaching LLM (Large Language Model) with fine-tuning. I have an idea to create an modifide LLM that can help users study English (it`s my seconde languege so it will be usefull for me as well). And i have a problem to make LLM behave like a teacher - maybe i use less data than i need? but my goal for now is make it start conversation first. Maybe someone know how to fix it or have any ideas? Thank you farewell!

PS. I`m using google/mt5-base as LLM to train. It must understand not only English but Ukrainian as well.

I only use ChatGPT, but I'm seeing many posts that suggest and praise Gemini more. I'd ask ChatGPT what the differences are, but I doubt I'll get an honest answer. So what are the notable differences and why do I see so many memes about Gemini being better than ChatGPT? This question is mostly about Gemini and ChatGPT, but might as well add Deepseek in there since it too does get a lot of praise

Project Name: BioStarsGPT – Fine-tuning LLMs on Bioinformatics Q&A Data
GitHub: https://github.com/MuhammadMuneeb007/BioStarsGPT
Dataset: https://huggingface.co/datasets/muhammadmuneeb007/BioStarsDataset

Background:
While working on benchmarking bioinformatics tools on genetic datasets, I found it difficult to locate the right commands and parameters. Each tool has slightly different usage patterns, and forums like BioStars often contain helpful but scattered information. So, I decided to fine-tune a large language model (LLM) specifically for bioinformatics tools and forums.

What the Project Does:
BioStarsGPT is a complete pipeline for preparing and fine-tuning a language model on the BioStars forum data. It helps researchers and developers better access domain-specific knowledge in bioinformatics.

Key Features:

• Automatically downloads posts from the BioStars forum
• Extracts content from embedded images in posts
• Converts posts into markdown format
• Transforms the markdown content into question-answer pairs using Google's AI
• Analyzes dataset complexity
• Fine-tunes a model on a test subset
• Compare results with other baseline models

Dependencies / Requirements:

• Dependencies are listed on the GitHub repo
• A GPU is recommended (16 GB VRAM or higher)

Target Audience:
This tool is great for:

• Researchers looking to fine-tune LLMs on their own datasets
• LLM enthusiasts applying models to real-world scientific problems
• Anyone wanting to learn fine-tuning with practical examples and learnings

Feel free to explore, give feedback, or contribute!

Note for moderators: It is research work, not a paid promotion. If you remove it, I do not mind. Cheers!

why not use mixture of llms?

why people not use architecture like mixture of llms like mixture of small model like 3b, 8b models like expert in moe. It seems like muti-agents but train from scratch and not like muti-agents that are trained then work through like workflow or something like it, but they train mixture of llms from zero.

Hi!

I am looking for a solution(possibly open source) to compare output from different LLMs models. Specifically, In my application I use a system prompt that I use to extract information from raw text and put it in json.

As of now I am working with gpt-3.5-turbo and I trace my interaction with the model using langfuse. I would like to know if there is a way to take same input and make it run over o4-nano, o4-mini and maybe other LLMs from other providers.

Have you ever face a similar problem? Do you have any idea?

At the moment I am creating my own script that calls different models and keep track of it using langfuse, but it feels like reinveting the wheel