LLM Benchmarking

In finance, the devil isn’t just in the details—it’s in the narrative. That’s what makes this new study by Md Talha Mohsin both timely and essential: it directly evaluates how five top-tier LLMs—GPT-4, Claude 4 Opus, Perplexity, Gemini, and DeepSeek—perform in interpreting the most linguistically dense and strategically revealing part of corporate disclosures: the Business section of 10-K filings from the “Magnificent Seven” tech giants. Rather than focusing on raw numbers or sentiment snippets, the study asks: can these LLMs extract strategic intent, infer risk, and assess future outlooks the way human analysts do? ...

In the race to make language models financial analysts, a new benchmark is calling bluff on the hype. FinanceBench, introduced by a team of researchers from Amazon and academia, aims to test LLMs not just on text summarization or sentiment analysis, but on their ability to think like Wall Street professionals. The results? Let’s just say GPT-4 may ace the chatroom, but it still struggles in the boardroom. The Benchmark We Actually Needed FinanceBench isn’t your typical leaderboard filler. Unlike prior datasets, which mostly rely on news headlines or synthetic financial prompts, this one uses real earnings call transcripts from over 130 public companies. It frames the task like a genuine investment analyst workflow: ...

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. ...