Papers

Introduction Imagine you read the sentence: “Mrs. Thompson gives her children some pasta.” As a human, your brain instantly performs a feat of abstraction. You understand that “pasta” is a type of “food.” Because you know she is giving “food,” you can infer a consequence: “The children are well-fed.” However, if you only viewed “pasta” as a physical object (an “entity”), the inference changes. If Mrs. Thompson gives an “entity,” the only safe inference is that the children “received an entity.” The nuance of feeding and being full is lost. ...

The explosion of Large Language Models (LLMs) has shifted the landscape of artificial intelligence. We have moved past the era where only tech giants could run these models. Today, open-source base models like LLaMA and Vicuna are readily available, allowing developers to fine-tune them for specific domains—be it finance, medicine, or mathematics. However, this democratization comes with a significant catch: Safety. When you take a base model and fine-tune it on a specific dataset (say, medical records), you run the risk of “catastrophic forgetting” regarding its safety protocols. A model that was once polite and harmless might, after fine-tuning, be tricked into generating malware code or hate speech. Traditionally, fixing this requires training-time alignment—processes like Reinforcement Learning from Human Feedback (RLHF). But RLHF is expensive, complex, and computationally heavy. ...

Introduction In the rapidly evolving world of Large Language Models (LLMs), we often focus on how well a model answers a question. But there is another side to the coin that is equally critical for training these models: how well can a model ask questions? To align LLMs with human expectations, developers need massive datasets of high-quality, multi-turn dialogues. Manually collecting these conversations is expensive and slow. The solution? Use LLMs to generate the data themselves. One LLM plays the “System Agent” (the chatbot), and another plays the “User Simulator” (the human). ...

If you have ever spent hours tweaking a prompt for a Large Language Model (LLM)—changing a word here, adding a constraint there, trying to get the model to “think” correctly—you have experienced the bottleneck of prompt engineering. We know that LLMs are capable of incredible reasoning, but their performance is often highly sensitive to the instructions they receive. While techniques like “Chain-of-Thought” (CoT) prompting significantly improve performance, they usually require humans to manually write out detailed reasoning steps. This is time-consuming and requires expertise. ...

Introduction Imagine you need to build a text classifier for a very specific task. Perhaps you need to filter emails that are both “urgent” and “related to shipping,” or identify social media posts that are “sarcastic” versus “genuinely angry.” Traditionally, you had two difficult options. First, you could collect thousands of examples and label them by hand to train a model—a slow, expensive process. Second, you could try “zero-shot” classification using raw text mining, which involves searching massive databases for keywords. However, this often fails when concepts are complex or nuanced. ...

Imagine you are texting a friend about a movie. Friend: “Have you seen Oppenheimer yet?” You: “Who is the director?” Friend: “Nolan.” You: “Oh, I love him.” To a human, this conversation is crystal clear. When you say “him,” you mean Christopher Nolan. When your friend says “Nolan,” they actually mean “Christopher Nolan is the director.” We constantly omit words (ellipsis) or use pronouns (coreference) because the context makes the meaning obvious. ...

How Contrastive Learning is Revolutionizing Event Causality Identification Causality is the bedrock of how humans understand the world. If we see a glass fall, we anticipate it might break. If we read that a heavy rainstorm occurred, we understand why the flight was delayed. For Artificial Intelligence, however, making these connections—specifically determining if one event explicitly caused another based on text—is a significant challenge. This task is known as Event Causality Identification (ECI). ...

Introduction Imagine you are teaching a child what a “red apple” is. You show them a picture of a red apple. Now, you want them to understand a “green chair.” You show them a green chair. Finally, you present them with a “green apple”—an object they haven’t explicitly studied before, but which is composed of concepts they already know (“green” and “apple”). If the child recognizes it, they have demonstrated Compositional Generalization. ...

Large Language Models (LLMs) like GPT-4 and Llama 2 have dazzled the world with their ability to write code, compose poetry, and answer complex questions. But there is a catch: these models perform best when they are on “familiar ground.” When you ask an LLM about popular topics—like the iPhone or major historical events—it shines. But what happens when you push the model into the obscure corners of knowledge, known as the long-tail distribution? ...

Introduction: The “Cocktail Party” Problem for AI Imagine you are at a loud, crowded party. Your friend is telling you a story. Despite the background music, the clinking of glasses, and the dozens of other conversations happening around you, you can perfectly understand what your friend is saying. You can strip away the noise, ignore the specific pitch of their voice, and focus entirely on the words and their meaning. ...

If you have ever been part of a busy group chat on WhatsApp or Slack, you know the chaos. Multiple conversations happen simultaneously. Someone answers a question from five minutes ago while two other people are debating lunch options. Keeping track of who is talking to whom—and more importantly, what they are talking about—is a significant cognitive task for humans. For Artificial Intelligence, this is a nightmare. In the field of Natural Language Processing (NLP), this problem is known as Multi-party Dialogue Generation (MDG). While standard chatbots (like early versions of Siri or simple customer service bots) only have to deal with one user (a one-on-one structure), MDG agents must navigate a web of entangled conversation threads. ...

Introduction If you have spent any time working with Large Language Models (LLMs), you have likely encountered the frustration of “prompt brittleness.” You spend hours crafting the perfect instruction, only to find that changing a single adjective or the order of examples drastically changes the output. This sensitivity is often seen as a bug, forcing engineers to hunt for a single “magic prompt” that solves a specific task. But what if we stopped trying to find the one perfect prompt? What if the sensitivity of LLMs to different instructions is actually a feature we can exploit? ...

Knowledge Graphs (KGs) are the silent engines powering much of the modern web. From Google’s Knowledge Vault to Wikidata, these massive networks store facts in the form of triples: (Head Entity, Relation, Tail Entity). For example, (Leonardo da Vinci, painted, Mona Lisa). However, KGs have a fundamental problem: they are never finished. Even the largest repositories suffer from incompleteness. This has given rise to the field of Knowledge Graph Completion (KGC)—the task of predicting missing links. ...

Introduction In the current era of Generative AI, training a Large Language Model (LLM) to speak fluent English is effectively a solved problem. The frontier has shifted from capability to alignment. We don’t just want models that can write; we want models that write in accordance with human values—being helpful, harmless, and honest. The industry standard for achieving this is Reinforcement Learning from Human Feedback (RLHF). This technique fine-tunes models using a “Reward Model” that acts as a proxy for human judgment. Think of the Reward Model as a judge: if the judge has a keen eye and clear values, the AI learns to behave well. If the judge is confused, inconsistent, or easily tricked, the AI learns the wrong lessons. ...

In the rapidly evolving world of Artificial Intelligence, we are witnessing a “survival of the fittest” regarding model size. Large Language Models (LLMs) like GPT-4 possess an emergent ability known as Chain-of-Thought (CoT) reasoning. Instead of just jumping to an answer, they break down complex problems into intermediate steps, much like a human showing their work on a math test. However, running these massive models is expensive and computationally heavy. This has led to a surge in research focused on Knowledge Distillation—teaching smaller, more efficient “Student” models (SLMs) to mimic the reasoning capabilities of “Teacher” LLMs. ...

Teaching Retrievers Logic: How Entailment Tuning is Solving the Relevance Gap in RAG If you have ever built a Retrieval-Augmented Generation (RAG) system or an open-domain Question Answering (QA) bot, you have likely encountered a frustrating phenomenon: the “keyword trap.” You ask your system a specific question, like “Who was the first person to step on the moon?” The retriever goes into your vector database and pulls out a passage. But instead of an article about Neil Armstrong’s historic step, it retrieves a biography that says, “Neil Armstrong loved looking at the moon as a child.” ...

Introduction The rapid rise of Large Language Models (LLMs) like ChatGPT and GitHub Copilot has fundamentally changed the landscape of software development. For professional developers, these tools are powerful productivity boosters. However, for computer science educators, they represent a looming crisis. In introductory programming courses (often called CS1 and CS2), the primary goal is to teach students the foundational logic of coding—loops, conditionals, and data structures. The problem? LLMs are exceptionally good at these standard problems. A student can copy a prompt, paste it into ChatGPT, and receive a working solution in seconds, bypassing the learning process entirely. ...

Introduction In the rapidly evolving landscape of Artificial Intelligence, Vision-Language Models (VLMs) have become superstars. Models like CLIP or GLIP can look at an image and describe it, or read a text description and find the corresponding object in a picture. They are powerful tools, pre-trained on massive datasets of image-text pairs scraped from the internet. However, this power comes with a significant catch: societal bias. Because these models learn from human-generated data, they often inherit our stereotypes. For example, a model might be more likely to associate a “kitchen” with a woman or a “workshop” with a man, regardless of who is actually in the picture. ...

Introduction There is an old adage that says, “an image is worth a thousand words.” However, if you look at how we currently train Artificial Intelligence to understand images, the reality is much closer to “an image is worth a dozen words.” State-of-the-art Vision-Language Models (VLMs)—the AI systems responsible for understanding photos and generating art—are largely trained on datasets scraped from the web. These datasets rely on “alt-text,” the short, often SEO-driven captions hidden in website code. While helpful, alt-text is rarely descriptive. It might say “Canon EOS R6” (camera metadata) or “Europe vacation” (location), but it rarely describes the visual scene, lighting, textures, or spatial relationships in detail. ...

When you show a picture of a Golden Retriever to a modern AI model like CLIP and it correctly identifies it as a “dog,” it’s easy to make assumptions about how it did that. We naturally assume the model “saw” the floppy ears, the golden fur, and the snout. We assume it matched the visual features of the image to the visual descriptions inherent in the word “dog.” But what if we’re wrong? What if the model isn’t looking at the dog at all, but rather looking for the digital equivalent of a watermark? Or what if it identifies the dog not by its shape, but by “knowing” it’s a pet that lives in North American suburbs? ...