Summary of Language Model Probabilities Are Not Calibrated in Numeric Contexts, by Charles Lovering et al.
Language Model Probabilities are Not Calibrated in Numeric Contexts
by Charles Lovering, Michael Krumdick, Viet Dac Lai, Seth Ebner, Nilesh Kumar, Varshini Reddy, Rik Koncel-Kedziorski, Chris Tanner
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research investigates whether language model (LM) outputs accurately capture natural distributions and numeric information within their textual contexts. The study focuses on evaluating LM calibration, examining how well LMs predict probabilities that align with the input context. For instance, if the prompt describes two equally likely options, the LM output probabilities should also be equal. Conversely, in scenarios where events are nonuniformly likely, the LM should output proportionate probabilities. The research finds that even top-performing LMs, such as gpt-4o-mini and Llama-3.1-8B, exhibit poor calibration and systematic biases, influenced by factors like word identity, order, and frequency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are meant to generate text based on input prompts, but have they been doing it correctly? A new study looks at how well language models predict probabilities within their context. Imagine you ask a model about the probability of heads or tails in a coin flip. The study wants to know if the model’s answer is correct and if it makes sense given the prompt. It found that even the best language models are not very good at this, and they tend to favor certain words or orders over others. This can lead to biased results. |
Keywords
» Artificial intelligence » Gpt » Language model » Llama » Probability » Prompt
