Why You Care
Ever wonder if AI truly ‘gets’ human emotion or the subtle complexity of a speech? Can Large Language Models (LLMs) really measure things like sentiment or political leaning accurately? A new study reveals some surprising answers about how LLMs handle these nuanced tasks, and why it matters for your data analysis.
This research, accepted to EMNLP 2025, dives into the strengths and weaknesses of using AI for social science measurements. It offers crucial insights for anyone relying on LLMs to understand human behavior or language. Understanding these findings can help you avoid common pitfalls and get more reliable results from your AI tools.
What Actually Happened
Researchers investigated how Large Language Models (LLMs) measure scalar constructs – continuous concepts like language complexity or emotionality. These constructs don’t just have simple ‘yes’ or ‘no’ answers; they exist on a spectrum. According to the announcement, the study evaluates four different approaches for LLM-based measurement in social science. The team used multiple datasets from political science literature for their comprehensive evaluation. They looked at methods including unweighted direct pointwise scoring and the aggregation of pairwise comparisons. They also examined token-probability-weighted pointwise scoring and finetuning smaller models. Each method offered different levels of accuracy and reliability when analyzing these continuous semantic structures. The goal was to find the most effective ways for LLMs to handle such nuanced data.
Why This Matters to You
If you’re using LLMs to analyze text, perhaps for sentiment analysis or understanding public opinion, this research directly impacts your results. The study yields actionable findings for applied researchers, as mentioned in the release. It highlights that directly asking an LLM for a score, like ‘rate this speech’s complexity from 1 to 10,’ often leads to skewed data. Imagine you’re analyzing customer feedback for emotional intensity. If your LLM lumps all ‘slightly negative’ comments into a single arbitrary score, your insights will be misleading. This is a crucial point for your data integrity.
What are the better approaches, then? The research shows that quality improves significantly with more methods. “The quality of the measurements improves with pairwise comparisons made by LLMs, but it improves even more by taking pointwise scores and weighting them by token probability,” the paper states. This means comparing two texts directly or considering the probability of each word choice leads to much better results. What’s more, the study indicates that even fine-tuning smaller models with just 1,000 training pairs can perform as well as or better than large, prompted LLMs. This is excellent news for those with limited resources. How might you adjust your current LLM workflows based on these findings to get more accurate data?
Here’s a quick overview of the methods and their effectiveness:
| Measurement Method | Direct Pointwise Scoring | Pairwise Comparisons | Token-Probability Weighted Pointwise Scoring | Fine-tuning Smaller Models |
| Accuracy | Low (discontinuous) | Medium | High | High (can exceed prompted LLMs) |
| Data Distribution | Bunched at arbitrary numbers | Smoother | Smoother, more continuous | Smoother, more continuous |
| Training Data Needed | Minimal | Minimal | Minimal | ~1,000 pairs |
The Surprising Finding
Here’s the twist: you might assume that directly asking an LLM for a numerical score is the most straightforward way to measure things. However, the study uncovered a significant issue with this approach. The team revealed that LLMs prompted to generate pointwise scores directly from texts produce discontinuous distributions. This means the scores aren’t spread out smoothly across the scale. Instead, they show ‘bunching’ at arbitrary numbers. Think of it as an LLM preferring to give scores like ‘5’ or ‘7’ much more often than ‘4’ or ‘6,’ even when the actual nuance suggests otherwise. This challenges the common assumption that LLMs can naturally handle continuous numerical outputs. This finding suggests a fundamental limitation in how LLMs process and represent scalar information when simply asked for a direct number. It’s a essential detail for anyone relying on these models for quantitative analysis of human language.
What Happens Next
This research, accepted to EMNLP 2025, suggests that future applications of LLMs in social science will likely move towards more measurement techniques. We can expect to see more tools and frameworks incorporating pairwise comparisons and token-probability weighting in the coming months and quarters. For example, a content analysis system might offer a ‘comparison mode’ where you feed it two articles, and it tells you which is more ‘persuasive’ rather than giving each a standalone persuasiveness score. For your own projects, consider experimenting with these methods. Actionable advice for readers includes moving beyond simple direct scoring. The study recommends exploring fine-tuning smaller, specialized models. This could be a cost-effective way to achieve high accuracy for your specific analytical needs. The industry implications are clear: developers of AI tools for social science must integrate these findings to build more reliable and accurate measurement capabilities.