Hey PaperLedge learning crew, Ernis here! Get ready to dive into some seriously cool research that's changing how we teach AI to think mathematically. We're talking about Large Language Models, or LLMs – those brainy algorithms that can generate text, translate languages, and even write different kinds of creative content. Remember how we talked about AI getting better at math?
Well, a lot of that improvement has come from using something called Reinforcement Learning (RL). Think of it like training a dog: you give it a treat (positive feedback) when it does something right, and maybe a "no" (negative feedback) when it messes up. The AI learns by trial and error, figuring out what actions lead to the best outcome. In the context of math, RL uses a simple "right" or "wrong" signal to guide the AI.
Now, Supervised Learning (SL) is a different approach. It's like showing a student a textbook full of solved problems. The AI learns by mimicking the correct answers. But here's the catch: traditionally, SL hasn't been very good at using wrong answers to learn. If the AI gets something wrong, you usually just throw that attempt away and move on. The general belief has been that using error feedback for self-improvement is something unique to RL.
But guess what? This paper challenges that idea! The researchers introduce a new method called Negative-aware Fine-Tuning (NFT). It's a clever twist on Supervised Learning that lets the AI learn from its mistakes – without needing a teacher to explicitly correct every error! Think of it like this: imagine you're learning to play chess. Instead of just studying winning games, you also analyze your losing games to see where you went wrong. That's the core idea behind NFT.
So, how does it work? Basically, instead of discarding those "wrong" answers, NFT uses them to create an implicit negative policy. Imagine you're building a map of "don't go there" zones based on your past mistakes. The AI essentially creates its own internal "bad example" guide. And the really cool part? This "bad example" guide is built using the same AI model we're trying to improve! This allows for something called direct policy optimization, which means the model can directly adjust its behavior based on both the good and bad examples it generates.
The researchers tested NFT on 7B and 32B parameter models in math reasoning tasks, and the results were impressive. NFT consistently outperformed standard SL methods, and even matched or surpassed some of the leading Reinforcement Learning algorithms! They even found that, under certain conditions, NFT and a specific RL algorithm (GRPO) are essentially doing the same thing, even though they come from completely different theoretical starting points! That's like discovering two completely different routes to the same destination.
Why does this matter?
For AI researchers: This bridges the gap between Supervised and Reinforcement Learning in systems that use simple right/wrong feedback. It opens up new avenues for developing more efficient and effective AI learning algorithms.
For educators: This shows that learning from mistakes is crucial, even for AI. It highlights the importance of providing learners with opportunities to reflect on their errors.
For anyone interested in AI safety: By understanding how AI learns from negative feedback, we can potentially develop safer and more reliable AI systems.
Here are a couple of questions that popped into my head while reading this:
Could NFT be applied to other areas beyond math, like coding or creative writing? What are the limitations?
If NFT and GRPO are equivalent under certain conditions, can we combine the best aspects of both approaches to create even more powerful learning algorithms?
This paper is a game-changer, showing that AI can indeed learn from its own failures in a supervised setting. It's a fascinating example of how researchers are constantly pushing the boundaries of what's possible with AI. Until next time, keep learning, keep questioning, and keep exploring the world of AI!Credit to Paper authors: Huayu Chen, Kaiwen Zheng, Qinsheng Zhang, Ganqu Cui, Yin Cui, Haotian Ye, Tsung-Yi Lin, Ming-Yu Liu, Jun Zhu, Haoxiang Wang

Hey everyone, Ernis here, and welcome back to PaperLedge! Today, we're diving into a fascinating paper that explores how we can make computer programs that can actually see and interact with the apps on our screens, just like we do. Think of it as teaching a computer to use a website or software program, not by coding, but by showing it how.
The paper focuses on something called LLM-based GUI agents. Let's break that down. LLM stands for Large Language Model. You've probably heard of these – they're the brains behind things like ChatGPT. GUI stands for Graphical User Interface – basically, anything you see on your screen that you can click on, like buttons, menus, and icons. So, we're talking about using these super smart AI language models to teach computers to use graphical interfaces.
Imagine you're trying to teach someone how to bake a cake. You could give them a recipe (code), or you could show them each step. That's what this research is about – teaching computers by demonstration. The problem is, getting enough examples of successful "cake-baking" (using apps) is really hard. Collecting those examples and figuring out what went right (or wrong!) is tough and time-consuming. This is where the paper gets interesting.
One of the big challenges is giving the computer the right kind of feedback. Existing methods use what's called an "Outcome Reward Model" (ORM). Imagine you're training a dog. An ORM is like only giving the dog a treat if it completely finishes the trick perfectly. If it messes up halfway through, no treat, even if it did most of it right! This can be discouraging and slow down the learning process. The problem is, it can punish good steps that were taken in a trajectory that ultimately failed.
This paper proposes something new: a "Progress Reward Model" or ProgRM. Instead of just rewarding the final outcome, ProgRM gives rewards along the way, based on how much progress the agent is making towards the goal. Think of it like giving the dog a small treat for each part of the trick it gets right. This gives the agent more information and helps it learn faster.
"ProgRM provides dense informative intermediate rewards by predicting a task completion progress for each step in online training."
So how do you figure out how much progress the agent is making? That's where another clever trick comes in: a "Longest Common Subsequence" (LCS) algorithm. This is a fancy way of saying they automatically figure out the key steps in a successful task by comparing different attempts and identifying the steps that are common to all of them. Then, they can reward the agent for taking those key steps.
For example, if you want to pay a bill online, some key steps might be:
Logging in to your account
Navigating to the bill payment section
Entering the payment amount
Confirming the payment
ProgRM is like automatically identifying those steps and giving the agent a "progress point" for completing each one. The team showed that agents trained with ProgRM did better than agents trained with existing methods, even outperforming some of the powerful AI models from big tech companies!
Why does this matter? Well, imagine a world where computers can easily learn how to use any software program, just by watching. This could make technology more accessible to everyone, especially people who struggle with complex interfaces. It could also automate many tasks, freeing up humans to focus on more creative and strategic work. For the everyday person, this could mean software that's easier to use and more customized to your needs. For businesses, it could mean more efficient workflows and reduced training costs. For developers, it could mean new ways to build and interact with software.
Here are a couple of questions that came to mind:
Could this technology eventually lead to AI assistants that can perform complex tasks across multiple applications, seamlessly switching between them to complete a goal?
What are the ethical implications of having AI agents that can automate tasks that are currently performed by humans? How do we ensure that this technology is used responsibly and doesn't lead to job displacement?
This research opens up a lot of exciting possibilities, and I'm eager to see where it goes. What do you think? Let me know in the comments!Credit to Paper authors: Danyang Zhang, Situo Zhang, Ziyue Yang, Zichen Zhu, Zihan Zhao, Ruisheng Cao, Lu Chen, Kai Yu

Hey learning crew, Ernis here, ready to dive into another fascinating paper! Today, we're tackling something that might sound a little dry at first – tabular data – but trust me, it gets really interesting when we throw in a dash of AI magic.
Now, you might be asking, "What's tabular data?" Think of it like an Excel spreadsheet, or a neatly organized table. This kind of data is everywhere, from medical records to financial reports. And for years, the undisputed champion for making sense of this data has been something called gradient boosting decision trees, or GBDTs. They're like super-smart flowcharts that can predict outcomes based on the patterns in the table.
But here's the thing: deep learning, the tech behind things like self-driving cars and super realistic AI art, has struggled to compete with GBDTs on tabular data. Until now, that is.
Researchers are working on what they're calling Tabular Foundation Models. Think of them as the Swiss Army knives of tabular data. They're designed to be adaptable and learn from a wide range of datasets, especially when that data includes free text, like doctor's notes or product reviews. This is where language models come in – the same kind of AI that powers chatbots and translation tools.
Now, previous attempts to combine language models with tabular data have been a bit... clumsy. They often used generic, one-size-fits-all text representations. It's like trying to understand a complex legal document by just looking at a list of keywords.
That's where this paper comes in. The researchers introduce TabSTAR, a new kind of Foundation Tabular Model that uses semantically target-aware representations. Sounds complicated, right? Let's break it down.
Imagine you're trying to predict whether a customer will leave a company based on their account activity and online reviews. TabSTAR doesn't just look at the words in the reviews; it focuses on what those words mean in the context of predicting customer churn. It's like having a detective who knows exactly what clues to look for.
The secret sauce is that TabSTAR "unfreezes" a pre-trained text encoder. This is like giving it a really good education in language before it even starts looking at the tabular data. Then, it feeds the model target tokens – these are key pieces of information about what it is trying to predict, so that it can learn task-specific embeddings.
The best part? TabSTAR is designed to work across different datasets without needing to be tweaked for each one. It's like having a universal translator that can understand any language.
The results are impressive. TabSTAR beats existing methods on several benchmark datasets, both medium and large. Plus, the researchers found that the more datasets they used to pre-train TabSTAR, the better it got. This means there's a clear path to even better performance in the future.
So, why should you care? Well, if you're a:
Data scientist: TabSTAR offers a powerful new tool for tackling tabular data with text features.
Business professional: This technology could lead to better predictions in areas like customer churn, fraud detection, and risk assessment.
Healthcare provider: Imagine using TabSTAR to analyze patient records and predict the likelihood of certain conditions.
Anyone interested in AI: This paper showcases the exciting progress being made in bridging the gap between deep learning and tabular data.
This research really opens up some interesting questions:
How can we make these models even more interpretable? One common criticism of deep learning is that it can be a "black box."
Could TabSTAR be adapted to work with other types of data, like images or audio?
What are the ethical implications of using these models to make decisions that impact people's lives? We always need to be mindful of bias and fairness.
That's it for this week's paper. I hope you found it insightful! Until next time, keep learning!Credit to Paper authors: Alan Arazi, Eilam Shapira, Roi Reichart

Hey learning crew, Ernis here, ready to dive into some fascinating research on how we're teaching AI to understand what we actually want! We're talking about large language models, those brainy bots that power chatbots and generate text. The big question is: how do we make sure they're not just smart, but also helpful and aligned with our values?
The answer, in a nutshell, is "Reinforcement Learning from Human Feedback," or RLHF. Think of it like training a puppy. You give it treats (positive feedback) when it does something good, and maybe a gentle "no" when it misbehaves. With RLHF, we're essentially training these AI models using human feedback to guide them toward better behavior. We train them to be more helpful, less toxic and more aligned with what we want as humans.
But here's the catch: it's easy to accidentally trick the system, leading to what researchers call "reward model overoptimisation." Imagine you're only rewarding the puppy for sitting perfectly still, even if it's uncomfortable. It might learn to sit very still, but it won't learn other important commands or how to interact naturally. Similarly, AI models can become overly focused on maximizing the reward signal, even if it means exploiting weird quirks or loopholes in the reward system. They become really good at gaming the system, rather than truly understanding what we want.
"Overoptimisation is when the AI focuses too much on the reward, and not enough on the actual task."
To combat this, many researchers use something called "iterated RLHF." It's like retraining the puppy with a slightly different approach each time. We update the feedback we're giving, and let the AI learn from its past mistakes. It’s like going back and revising your study notes after a practice test – you refine your understanding based on your previous performance.
Now, this is where the research we're discussing today comes in. A team of scientists has been digging deep into how this "iterated RLHF" process actually works, and what factors can make it more effective. They used a controlled environment called "AlpacaFarm" to systematically test different strategies. AlpacaFarm is like a virtual playground where researchers can try different ways of training AI without real-world consequences.
One key question they explored was how to transfer the data from one training iteration to the next. Should we start fresh each time, or build on what the AI has already learned? They found that while starting from scratch can be more robust, it can also limit the AI's potential for improvement. Imagine always restarting your essay from the very beginning – you might avoid major errors, but you'll also miss out on the chance to develop more nuanced and sophisticated arguments.
The researchers also looked at different ways of initializing the AI at the beginning of each iteration. They found that reinitializing from the "base policy" (the AI's original state before any training) is pretty safe, but it doesn't allow for much flexibility. Other initialization strategies can be riskier, especially if the AI has already fallen into the trap of overoptimisation early on.
So, why does all this matter? Well, for those of you working directly with AI, these findings offer practical tips for building more stable and generalizable RLHF pipelines. For the rest of us, it's a reminder that training AI is not just about throwing data at it. It's about carefully designing the feedback process to ensure that the AI is learning the right things, and not just finding clever ways to game the system.
Ultimately, this research helps us build AI systems that are not just intelligent, but also aligned with our values and goals. And that's something we can all get behind.
What are the ethical considerations of using human feedback to train AI, especially when that feedback might be biased or subjective?
How can we design reward systems that are less susceptible to overoptimisation and more reflective of real-world complexity?
As AI becomes more integrated into our lives, how do we ensure that it continues to learn and adapt to our evolving needs and values?
Credit to Paper authors: Lorenz Wolf, Robert Kirk, Mirco Musolesi

Hey PaperLedge crew, Ernis here, ready to dive into some brainy stuff that's surprisingly relevant to our everyday lives. Today, we're talking about how well Large Language Models – those mega-smart AIs like ChatGPT – can find a single, important piece of information hidden in a mountain of irrelevant data. Think of it like finding a specific grain of sand on a whole beach! That's what researchers call a "needle-in-a-haystack" task.
Now, you might think these LLMs are super-human at sifting through data. But... they're not perfect! Turns out, they struggle with this "needle-in-a-haystack" problem. We already knew that where the needle is hidden (positional bias) and how much distracting stuff there is (distractor quantity) throws them off. But, here's the kicker: a recent paper asks, "What happens when the needle itself is really, really small?"
Let's say the "needle" is the key piece of information needed to answer a question. This paper dug into how the size of that key piece affects the LLM's ability to find it. Imagine you're looking for the answer to a question, and the answer is just a tiny phrase buried in a huge document. Is that harder than if the answer is a longer, more detailed explanation?
Well, guess what? The researchers found that when the "needle" – that crucial bit of information – is shorter, the LLM's performance takes a nosedive! Smaller "needles" consistently mess with the LLMs' ability to pinpoint the right answer, and it makes them even more sensitive to where the information is located in the haystack.
"LLM performance drops sharply when the gold context is shorter...smaller gold contexts consistently degrade model performance and amplify positional sensitivity."
This isn't just some abstract computer science problem. Think about it: this has huge implications for AI assistants that need to pull together information from all over the place to answer your questions. If the crucial details are scattered and brief, these systems are more likely to miss them. This pattern applies in different situations like general knowledge quizzes, complicated medical questions, and even math problems!
The researchers tested this across seven different state-of-the-art LLMs, big and small, and saw the same pattern. This means it's a pretty fundamental limitation of how these models work right now.
So, why should you care? Well, if you're a:
Student: You're relying on AI to help you research and summarize information. This research suggests you need to be extra careful to double-check the AI's findings, especially when the key information is concise.
Healthcare Professional: Imagine using AI to quickly find crucial details in patient records. This study highlights the risk of missing important but brief pieces of information, potentially leading to misdiagnosis or incorrect treatment plans.
Developer building AI applications: This is a wake-up call! We need to design these systems to be more robust and less sensitive to the size and location of key information.
This study is important because it gives us a clearer picture of the strengths and weaknesses of LLMs. It highlights that we can't just throw more data at these models and expect them to magically find the right answer. We need to understand their limitations and design them to be more reliable, especially when dealing with scattered, concise information.
Here are a few questions this research brings up for me:
If shorter "needles" are harder to find, can we train LLMs to be better at identifying and prioritizing concise, impactful information?
Could different prompting strategies or retrieval methods help LLMs overcome this sensitivity to gold context length?
How can we best evaluate LLMs to ensure they are reliably finding all the relevant information, even when it's buried deep in the haystack?
That's all for this week's deep dive! Keep learning, keep questioning, and I'll catch you on the next PaperLedge!Credit to Paper authors: Owen Bianchi, Mathew J. Koretsky, Maya Willey, Chelsea X. Alvarado, Tanay Nayak, Adi Asija, Nicole Kuznetsov, Mike A. Nalls, Faraz Faghri, Daniel Khashabi

Hey PaperLedge crew, Ernis here, ready to dive into another fascinating piece of research! Today, we're exploring something truly unique: how well can artificial intelligence, specifically those big language models (LLMs) we keep hearing about, actually understand Arabic poetry?
Now, Arabic poetry isn't just any old poetry. It's like a cultural fingerprint, packed with history, complex meanings, and a huge variety of styles. Think of it as the ultimate test for a language model. It's not enough to just translate words; you need to grasp the subtle nuances, the metaphors, the rhythm, and even the cultural context. Imagine trying to explain a Shakespeare sonnet to someone who's never heard of love or England – that's the kind of challenge we're talking about!
So, a team of researchers created a new benchmark called Fann or Flop. Think of a benchmark as a standardized test for AI. This one is special because it focuses specifically on Arabic poetry from twelve different historical periods, covering everything from classical forms to modern free verse. That's like testing an AI on everything from Homer to hip-hop!
This benchmark includes poems with explanations that cover:
Semantic Understanding: Can the AI grasp the literal meaning of the words?
Metaphor Interpretation: Can it understand what the poet really means beyond the surface? Think of "My love is a rose." It's not literally a rose, right?
Prosodic Awareness: Can it recognize the rhythm and rhyme schemes, the musicality of the verse?
Cultural Context: Does it understand the historical and social background that influenced the poem?
The researchers argue that understanding poetry is a really good way to test how well an AI truly understands Arabic. It's like saying, "If you can understand this, you can understand anything!" It goes way beyond simple translation or answering basic questions. It requires deep interpretive reasoning and cultural sensitivity. Think of it as the difference between reciting a recipe and actually understanding how to cook.
Here's the kicker: The researchers tested some of the most advanced LLMs on this benchmark, and guess what? They mostly flopped! Even though these models are super impressive on standard Arabic language tasks, they struggled to truly understand the poetry. This tells us that these AIs are good at processing information, but they're not quite ready to appreciate the art and cultural depth of Arabic poetry.
"Poetic comprehension offers a strong indicator for testing how good the LLM is in understanding classical Arabic... Unlike surface-level tasks, this domain demands deeper interpretive reasoning and cultural sensitivity."
The good news is that the researchers have made Fann or Flop available as an open-source resource. This means anyone can use it to test and improve Arabic language models. It’s like giving the AI community a new tool to unlock a deeper understanding of Arabic language and culture.
You can even check out the code yourself here: https://github.com/mbzuai-oryx/FannOrFlop
So, why does this matter? Well, for AI developers, it highlights the limitations of current models and points the way towards building more sophisticated and culturally aware AI systems. For linguists and cultural scholars, it provides a new tool for exploring the richness and complexity of Arabic poetry. And for anyone interested in AI ethics, it raises important questions about the need for cultural sensitivity in AI development.
Here are some things that really stood out to me:
This challenges the idea that if an AI is good at language translation, it's also good at understanding culture. It makes you wonder, what else are we missing?
It shows that there's still a huge gap between AI's ability to process information and its ability to truly understand human expression.
The fact that the researchers released this as open-source is amazing, because it means that anyone can contribute to making AI more culturally aware.
And that gets me thinking...
First, if AI struggles with something as structured as poetry, what does that say about its ability to understand more nuanced forms of communication, like sarcasm or humor?
Second, how can we ensure that AI models are developed with a deep understanding and respect for different cultures?
Finally, what other "cultural benchmarks" could we create to test AI's understanding of different aspects of human culture?
I hope you found that as fascinating as I did! Until next time, keep learning!Credit to Paper authors: Wafa Alghallabi, Ritesh Thawkar, Sara Ghaboura, Ketan More, Omkar Thawakar, Hisham Cholakkal, Salman Khan, Rao Muhammad Anwer

Hey PaperLedge crew, Ernis here! Get ready to dive into some fascinating research that's all about making AI assistants more trustworthy. You know how Large Language Models, or LLMs, like the ones powering your favorite chatbots, are getting super smart?
But, sometimes, even the smartest LLM needs a little help from its friends – think of it like this: the LLM is a super-enthusiastic student, but it needs access to the library (external tools) to ace the exam.
This paper tackles a really important question: How do we know we can trust what these LLMs tell us, especially when they're using external tools to find information? If an LLM is helping a doctor make a diagnosis, we need to be absolutely sure it's giving accurate advice. This is where "uncertainty" comes in. It's like a little flag that says, "Hey, I'm not 100% sure about this."
The problem is that existing ways of measuring uncertainty don't really work when the LLM is using tools. It's like trying to measure the temperature of a cake without considering the oven! We need to consider both the LLM's confidence and the tool's reliability.
So, what did these researchers do? They created a new framework that takes both the LLM and the external tool into account when figuring out how uncertain the final answer is. Think of it as building a better thermometer for that cake, one that considers both the batter and the oven temperature.
They've built something that works like a "trust-o-meter" for these systems.
They’ve made the calculations speedy enough to actually use in real-world situations.
"Our results show that the framework is effective in enhancing trust in LLM-based systems, especially in cases where the LLM's internal knowledge is insufficient and external tools are required."
To test their framework, they created some special practice questions – it's like giving the LLM and its tools a pop quiz! These questions were designed to require the LLM to use external tools to find the right answer.
They even tested it out on a system that uses "Retrieval-Augmented Generation" or RAG. RAG is like giving the LLM a cheat sheet – it searches for relevant information before answering. The researchers showed that their uncertainty metrics could help identify when the LLM needed that extra information.
In essence, this research is all about making AI more reliable and trustworthy, especially when it's being used in important areas like healthcare or finance. It's about building systems that are not only smart but also honest about what they don't know.
Now, thinking about this research, a few questions popped into my head:
How can we explain this concept of uncertainty to people who aren't technical experts? Is there a good analogy we can use?
Could this framework be used to train LLMs to be more aware of their own limitations?
What are some of the ethical implications of using these tools, and how do we ensure they're used responsibly?
That’s all for this paper summary, folks! I hope you found it interesting. Let me know what you think, and keep learning!Credit to Paper authors: Panagiotis Lymperopoulos, Vasanth Sarathy

Hey PaperLedge Learning Crew, Ernis here, ready to dive into some fascinating research! Today, we're unpacking a study that looks at how social media chatter influences where we choose to travel. Think of it like this: remember the last time you saw a friend's amazing vacation photos and suddenly needed to visit that same place? That’s user-generated content, or UGC, in action!
Now, all this travel inspiration floating around online is a goldmine of information for tourism companies. But sifting through it all—millions of posts, reviews, and comments—is a huge task. That’s where the researchers come in. They wanted to find a way to automatically understand what people expect from their travel experiences based on what they're sharing online.
So, how did they do it? They used something called a Large Language Model, or LLM. Think of an LLM like a super-smart parrot that’s read pretty much the entire internet. It can understand and generate human-like text.
This study used a clever two-step approach with their LLM. First, they let the LLM loose on a pile of UGC to identify common expectations people had, all on its own, like an unsupervised learner. Then, they took what the LLM found and fine-tuned it using data from surveys to make it even more accurate, like a supervised learner. It’s like teaching our super-parrot to not just repeat what it hears, but to actually understand what it's saying!
The big takeaway? The researchers found that leisure and social expectations - things like wanting to relax or connect with friends - are bigger drivers of travel decisions than basic needs like beautiful scenery or even emotional factors like feeling peaceful. That's wild, right? It suggests that sharing experiences with others, and showing off your fun adventures, is a huge part of why people choose to travel in the first place.
"By establishing LLMs as precision tools for expectation quantification, we advance tourism analytics methodology and propose targeted strategies for experience personalization and social travel promotion."
In other words, understanding these social motivations can help tourism companies tailor experiences and promotions that really resonate with potential travelers. Imagine targeted ads showing groups of friends laughing on a beach, instead of just pictures of the beach itself.
But here's the really cool part: this LLM framework isn't just for tourism! It can be adapted to understand consumer behavior in all sorts of areas. Think about how companies could use this to figure out what people expect from a new phone, a new car, or even a new type of food. It's a powerful tool for understanding what makes people tick.
This research highlights the transformative potential of computational social science. By using computers to analyze human behavior at scale, we can gain valuable insights into what motivates us and how we make decisions.
Why does this matter to you, the listener?
For marketers: This is a game-changer for targeted advertising and personalization.
For travelers: Expect more tailored and relevant travel recommendations based on your social interests.
For anyone interested in social trends: This shows how our online behavior shapes real-world decisions.
So, here are a couple of things I was pondering as I read this research:
Could these LLMs also be used to predict future travel trends based on emerging social media conversations?
Does the emphasis on social expectations lead to a pressure to curate perfect travel experiences for online sharing, potentially diminishing the authentic joy of travel?
Let me know what you think, Learning Crew! What other questions does this research spark for you? Until next time, keep exploring!Credit to Paper authors: Haotian Lan, Yao Gao, Yujun Cheng, Wei Yuan, Kun Wang