Summary of Contextualized Sequence Likelihood: Enhanced Confidence Scores For Natural Language Generation, by Zhen Lin et al.

Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation

by Zhen Lin, Shubhendu Trivedi, Jimeng Sun

First submitted to arxiv on: 3 Jun 2024

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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 paper proposes enhancing the confidence score function for large language models (LLMs) in natural language generation tasks. The current likelihood-based approach conflates semantic and syntactic components, leading to inaccuracies. To address this, the authors introduce the Contextualized Sequence Likelihood (CSL), which assigns different weights to tokens based on attention values from the base LLM. This new score is easy to implement, fast to compute, and improves reliability in predicting generation quality. The authors demonstrate CSL’s effectiveness across several question-answering datasets and a range of LLMs, achieving significantly higher reliability than state-of-the-art baselines as measured by AUROC or AUARC.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper is about making computer language models more reliable. Right now, these models are good at generating text, but they’re not great at knowing how sure they are about what they’re saying. This matters because sometimes the model might say something that’s correct, but not very well expressed. The authors want to fix this by changing how the model calculates its confidence score. They propose a new method called Contextualized Sequence Likelihood (CSL) that takes into account how important each word is in the sentence. This helps the model be more accurate and reliable in what it says. The authors tested their method on several datasets and showed that it works better than other methods.