Fine-Tuning

What makes a large language model (LLM) biased? Is it the instruction tuning data, the randomness of training, or something more deeply embedded? A new paper from Itzhak, Belinkov, and Stanovsky, presented at COLM 2025, delivers a clear verdict: pretraining is the primary source of cognitive biases in LLMs. The implications of this are far-reaching — and perhaps more uncomfortable than many developers would like to admit. The Setup: Two Steps, One Core Question The authors dissected the origins of 32 cognitive biases in LLMs using a controlled two-step causal framework: ...

When Meta and other tech giants scale back content moderation, the gap isn’t just technical—it’s societal. Civil society organizations (CSOs), not corporations, are increasingly on the frontlines of monitoring online extremism. But they’re often armed with clunky tools, academic prototypes, or opaque black-box models. A new initiative—highlighted in Civil Society in the Loop—challenges this status quo by co-designing a Telegram monitoring tool that embeds human feedback directly into its LLM-assisted classification system. The twist? It invites civil society into the machine learning loop, not just the results screen. ...

In the world of LLM fine-tuning, stronger usually means better. But what if we’ve been looking at supervision all wrong? A provocative new paper introduces the Delta Learning Hypothesis, arguing that LLMs can learn just as well—sometimes even better—from weak data, as long as it’s paired. The trick isn’t in the absolute quality of the training signals, but in the difference—the delta—between them. Like a coach pointing out small improvements, even bad examples can teach if they highlight how one is slightly better than another. ...

Why rent a Ferrari when a fine-tuned e-bike can get you to class faster, cheaper, and on your own terms? That’s the question quietly reshaping AI in education, as shown by Solano et al. (2025) in their paper Narrowing the Gap. The authors demonstrate that with supervised fine-tuning (SFT), smaller open-source models like Llama-3.1-8B and Qwen3-4B can rival proprietary giants like GPT-4.1 when explaining C programming error messages to students. More strikingly, they achieve this with better privacy, lower cost, and pedagogical nuance that large models often overshoot. ...

The financial industry has always prided itself on cold precision. For decades, quantitative models and spreadsheets dominated boardrooms and trading desks. But that orthodoxy is now under siege. Not from another statistical breakthrough, but from something surprisingly human-like: Large Language Models (LLMs). Recent research shows a dramatic shift in how AI—particularly LLMs like GPT-4 and LLaMA—is being integrated across financial workflows. Far from just summarizing news or answering earnings call questions, LLMs are now organizing entire investment pipelines, fine-tuning themselves on proprietary data, and even collaborating as autonomous financial agents. A recent survey by Mahdavi et al. (2025) categorized over 70 state-of-the-art systems into four distinct architectural frameworks, offering us a lens through which to assess the future of financial AI. ...

General-purpose language models can solve math puzzles and explain Kant, but struggle to identify a ticker or classify earnings tone. What the financial world needs isn’t more reasoning—it’s better reading. Over the past year, large language models (LLMs) have surged into every corner of applied AI, and finance is no exception. But while the promise of “reasoning engines” captivates headlines, the pain point for financial tasks is much simpler—and more niche. ...

The next generation of quantitative investment agents must be more than data-driven—they must be logic-aware and structurally adaptive. Two recently published research efforts provide important insights into how reasoning patterns and evolving workflows can be integrated to create intelligent, verticalized financial agents. Kimina-Prover explores how reinforcement learning can embed formal reasoning capabilities within a language model for theorem proving. Learning to Be a Doctor shows how workflows can evolve dynamically based on diagnostic feedback, creating adaptable multi-agent frameworks. While each stems from distinct domains—formal logic and medical diagnostics—their approaches are deeply relevant to two classic quant strategies: the Black-Litterman portfolio optimizer and a sentiment/technical-driven Bitcoin perpetual futures trader. ...