Summary of Confidence Estimation For Llm-based Dialogue State Tracking, by Yi-jyun Sun et al.
Confidence Estimation for LLM-Based Dialogue State Tracking
by Yi-Jyun Sun, Suvodip Dey, Dilek Hakkani-Tur, Gokhan Tur
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates methods to quantify and leverage uncertainty in large language models (LLMs) for conversational AI systems. Specifically, it focuses on dialogue state tracking (DST) in task-oriented dialogue systems (TODS). The authors explore approaches for open- and closed-weight LLMs, emphasizing the importance of well-calibrated confidence scores to improve model performance. They evaluate four methods based on softmax, raw token scores, verbalized confidences, and their combination using the area under the curve (AUC) metric. The findings suggest that fine-tuning open-weight LLMs can lead to better calibration and improved joint goal accuracy (JGA). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make computers talk more intelligently with humans. It’s important because sometimes these computers say things they’re not sure are true, which isn’t helpful. The researchers looked at different methods to figure out how confident the computer model is in what it says. They tested these methods on a special kind of computer model that can have conversations. The results show that one way of making the models better is by fine-tuning them for this specific task. |
Keywords
» Artificial intelligence » Auc » Fine tuning » Softmax » Token » Tracking
