A model is considered well-calibrated when its probability estimate aligns
with the actual likelihood of the output being correct. Calibrating language
models (LMs) is crucial, as it plays a vital role in detecting and mitigating
hallucinations, a common issue of LMs, as well as building more trustworthy
models. Yet, popular neural model calibration techniques are not well-suited
for LMs due to their lack of flexibility in discerning answer correctness and
their high computational costs. For instance, post-processing methods like
temperature scaling are often unable to reorder the candidate generations.
Moreover, training-based methods require finetuning the entire model, which is
impractical due to the increasing sizes of modern LMs. In this paper, we
present LitCab, a lightweight calibration mechanism consisting of a single
linear layer taking the input text representation and manipulateing the LM
output logits. LitCab improves model calibration by only adding < 2% of the
original model parameters. For evaluation, we construct CaT, a benchmark
consisting of 7 text generation tasks, covering responses ranging from short
phrases to paragraphs. We test LitCab with Llama2-7B, where it improves
calibration across all tasks, by reducing the average ECE score by 20%. We
further conduct a comprehensive evaluation with 7 popular open-sourced LMs from
GPT and LLaMA families, yielding the following key findings: (1) Larger models
within the same family exhibit better calibration on tasks with short
generation tasks, but not necessarily for longer ones. (2) GPT-family models
show superior calibration compared to LLaMA, Llama2 and Vicuna models despite
having much fewer parameters. (3) Finetuning pretrained model (e.g., LLaMA)
with samples of limited purpose (e.g., conversations) may lead to worse
calibration, highlighting the importance of finetuning setups for calibrating
LMs.

LitCab 是一种轻量级的校准机制，通过仅增加不到原始模型参数数量的 2% 的线性层对输入文本表示进行操作，改善了模型校准，提升了在不同文本生成任务上的性能，并对不同模型进行综合评估，得出了关于模型校准的关键发现。