Papers

Introduction In the era of Large Language Models (LLMs), In-Context Learning (ICL) has become a dominant paradigm. The idea is deceptively simple: instead of fine-tuning a model’s weights, you simply provide a few examples (exemplars) in the prompt, and the model learns the pattern. For example, if you want an LLM to translate English to SQL, your prompt might look like this: Input: Show me users over 20. Output: SELECT * FROM users WHERE age > 20; ...

Introduction Imagine you are handed a 50-page transcript of a corporate meeting and asked a single question: “Why did the marketing team disagree with the engineering team about the budget?” To answer this, you wouldn’t summarize the entire meeting. You wouldn’t care about the opening pleasantries, the coffee break discussions, or the unrelated IT updates. You would scan the document, identify the specific segments where marketing and engineering discussed finances, rank them by importance, and then synthesize an answer. ...

In the vast ocean of unstructured text on the internet—Wikipedia pages, news articles, financial reports—lies a treasure trove of data waiting to be organized. For years, the field of Information Extraction (IE) has been the miner of this digital age, digging through paragraphs to find relationships between things. Traditionally, this has been done by hunting for “triplets”: a Subject, a Relation, and an Object (e.g., Bill Gates, Co-founder, Microsoft). While effective, this approach has limits. It treats information as a bag of disconnected facts rather than a cohesive profile of an entity. ...

Introduction In the current landscape of Artificial Intelligence, Large Language Models (LLMs) like GPT-4, Claude, and Gemini have become ubiquitous. They possess incredible reasoning capabilities, yet they remain prone to hallucinations, biases, and reasoning errors. For researchers and engineers, the standard solution to these errors is usually fine-tuning or steering the model. However, a significant barrier exists: most state-of-the-art models are black boxes. We interact with them via APIs, sending a prompt and receiving text. We do not have access to the model’s weights, gradients, or often even the output token probabilities (logits). This opacity makes traditional adaptation methods—which rely on accessing internal model states—impossible. ...

Imagine you are a data scientist at a massive e-commerce aggregator. You have a database of products from Amazon and another from Google Shopping. Your task is to merge them. On one side, you have a record: iPhone 13, 128GB, Midnight. On the other side: Apple iPhone 13 - Black - 128 GB Storage. To a human, these are obviously the same product. But to a machine, they are just strings of characters. This is the problem of Entity Matching (EM)—identifying when different records refer to the same real-world entity. ...

Baking System 2 Thinking into LLMs: How Offline Simulation Improves Reasoning Large Language Models (LLMs) like GPT-4 and Llama 2 have dazzled the world with their ability to write poetry, code, and essays. However, when it comes to rigorous logical reasoning or complex multi-step mathematics, cracks often appear in the facade. The model might hallucinate facts, make logical leaps that don’t follow, or simply guess the final answer without understanding the “why.” ...

In the world of Artificial Intelligence, we have become very good at generating text. Models like GPT-4 and LLaMA-2 can write poetry, code, and short stories with ease. However, evaluating that text remains a massive hurdle. In objective tasks like translation or summarization, we have ground truths to compare against. But what about creative writing? If I write a story with a tragic, ambiguous ending, is it “good”? One reader might call it “poignant and realistic,” while another dismisses it as “depressing and unsatisfying.” ...

Unlocking Judicial Fairness: How LLMs and Legal Knowledge are Revolutionizing Case Retrieval In the legal world, the concept of stare decisis—standing by things decided—is foundational. For judges and lawyers, sourcing relevant precedents is not just a research task; it is a critical requirement for upholding judicial fairness. If a judge cannot find a past case that mirrors the current one, the consistency of the law is at risk. However, finding that “needle in a haystack” is becoming increasingly difficult. Legal case retrieval is significantly different from typing a question into Google. The “queries” are often entire case documents describing a new situation, and the “documents” to be retrieved are lengthy, complex judgments from the past. These documents are filled with specialized jargon, intricate procedural details, and often contain multiple different crimes within a single text. ...

Introduction: The Problem with the Confident Liar If you have spent any time interacting with Large Language Models (LLMs) like ChatGPT or LLaMA, you have likely encountered a specific, frustrating behavior: the confident hallucination. You ask a question about a niche topic, a fictional character, or a specific medical condition, and the model responds with absolute certainty. It sounds plausible, the grammar is perfect, and the logic seems sound. But there is one problem—the facts are completely made up. ...

If you have ever tutored a student in mathematics, you know there is a distinct difference between memorization and understanding. A student who memorizes might be able to solve a specific quadratic equation because they’ve seen that exact pattern fifty times. But if you ask them, “How would this change if the coefficient was negative?” or “Can you solve this using a different method?”, they crumble. They have the answer, but they don’t have the reasoning depth. ...

Introduction We have become accustomed to the “magic” of Large Language Models (LLMs). You type a prompt—whether it’s a request to translate a sentence, summarize a paragraph, or analyze the sentiment of a review—and the model obliges. But beneath the surface, what is actually happening inside the neural network? While we know how to train these models—feeding them massive datasets and refining them with instruction tuning—we often treat the resulting model as a “black box.” We know the input and the output, but the internal mechanics of where and when the model decides to perform a specific task remain largely a mystery. Does the model know it is performing a translation in the very first layer? or does that realization happen right before the final output? ...

Introduction In the past few years, the headline “AI Passes the Bar Exam” has appeared in nearly every major tech publication. It is a compelling narrative: Large Language Models (LLMs) like GPT-4 have ingested so much information that they can technically qualify to practice law. But any practicing attorney will tell you that passing a standardized test and navigating the nuanced, high-stakes reality of the legal system are two very different things. ...

Introduction Deep learning models, particularly Large Language Models (LLMs) like BERT, RoBERTa, and Llama, have achieved superhuman performance on a vast array of Natural Language Processing (NLP) tasks. Yet, despite their brilliance, they suffer from a significant flaw: they are “black boxes.” We feed them a sentence, and they spit out a prediction, but the internal reasoning process remains largely opaque. For students and researchers aiming to build safe and trustworthy AI, this opacity is a problem. How do we trust a model if we don’t know why it made a decision? ...

If you have ever trained a machine learning model, you know the pain. You have a brilliant architecture and a clear objective, but you hit the inevitable bottleneck: data. specifically, labeled data. For years, the gold standard for getting high-quality labels was human annotation. Whether relying on expensive domain experts (like doctors labeling X-rays) or crowdsourcing platforms (like Amazon Mechanical Turk), the process is slow, costly, and often inconsistent. But we are currently witnessing a paradigm shift. The very models that consume data—Large Language Models (LLMs)—are now capable of producing it. ...

If you have used a search engine recently, you have likely benefited from dense retrieval. Unlike the search engines of the 90s that looked for exact keyword matches, modern systems try to understand the meaning behind your query. They turn your words into a list of numbers (a vector) and look for documents with similar vectors. For years, the backbone of this technology has been models like BERT and T5. They are excellent at what they do, but they have hit a ceiling. They struggle with long documents, they often fail when presented with data from a new domain (like legal or medical documents), and they require massive amounts of labeled data to train. ...

Imagine you are looking at a photograph of a bustling city street. In the background, there is a bus. A friend asks you, “What is the name of the bus company?” To answer, your eyes immediately filter out the pedestrians, the buildings, the traffic lights, and the clouds. You focus entirely on the logo printed on the side of the bus. For humans, this selective attention is instinctual. For Artificial Intelligence, specifically Visual Question Answering (VQA) systems, it is incredibly difficult. When presented with a complex image, traditional AI models often get “distracted” by the most prominent objects (like the pedestrians) rather than the specific detail required to answer the question (the logo). ...

Introduction We have all been there. You ask a Large Language Model (LLM) a complex question—perhaps a tricky math word problem or an obscure trivia query—and it confidently gives you an answer. It looks plausible. The reasoning seems sound. But then, upon closer inspection, you realize the answer is completely wrong. This phenomenon, often called hallucination or reasoning failure, is one of the biggest hurdles in deploying AI agents for high-stakes tasks. The natural solution seems to be: “Why don’t we just ask the model to double-check its work?” ...

Introduction: The Dilemma of the Expert LLM The explosion of Large Language Models (LLMs) has changed the landscape of artificial intelligence. We have moved past general-purpose chatbots to an era of specialized experts—models like BloombergGPT for finance or Med-PaLM for medicine. To create these experts, we take a general model (like LLaMA) and fine-tune it on domain-specific data. But here lies a critical problem: Domain-specific data is often sensitive. To train a medical LLM effectively, you need real medical records. To train a financial advisor, you need real transaction histories. This data is riddled with Personally Identifiable Information (PII)—names, addresses, social security numbers, and specific medical conditions. ...

Introduction In the past year, headlines have been dominated by the impressive feats of Large Language Models (LLMs) in the medical field. We’ve seen reports of AI passing the United States Medical Licensing Examination (USMLE) with flying colors, performing on par with—or sometimes even better than—human experts on standardized tests. It is easy to look at these results and assume that we are on the brink of an AI revolution in daily clinical practice. ...

Lying is harder than telling the truth. To tell the truth, you simply recall a fact or perform a logical deduction. To tell a lie—specifically a convincing one—you must know the truth, deliberately suppress it, fabricate a plausible alternative, and ensure the fabrication maintains internal consistency. It is a complex cognitive task. We often assume that Large Language Models (LLMs) are masters of hallucination, capable of spinning wild tales or getting facts wrong. However, a fascinating new research paper titled “Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks” reveals a paradoxical weakness in these systems: they struggle to lie on purpose. ...