Large language models (LLMs) often exhibit excessive, random, and uninformative uncertainty, rendering them unsuitable for decisionmaking in human-computer interactions.In this paper, we aim to instigate a heightened awareness of self-uncertainty in LLMs, enabling them to express uncertainty more effectively.To accomplish this, we propose an uncertainty-aware instruction tuning (UaIT) method, aligning LLMs' perception with the probabilistic uncertainty of the generation.We conducted experiments using LLaMA2 and Mistral on multiple free-form QA tasks.Experimental results revealed a surprising 45.2% improvement in the effectiveness of uncertainty expression by LLMs, accompanied by reasonably good out-of-domain generalization capabilities.Moreover, this uncertainty expression can serve as a valuable real-time basis for human decision-making, e.g., retrieving external documents and incorporating stronger LLMs 1 .

Shudong Liu, Xuebo Liu, Derek F. Wong, Zhaocong Li, Wenxiang Jiao, Lidia S. Chao, Min Zhang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets.However, existing summarization benchmarks overlap in time with the standard pre-training corpora and finetuning datasets.Hence, the strong performance of PLMs may rely on the parametric knowledge that is memorized during pre-training and fine-tuning.Moreover, the knowledge memorized by PLMs may quickly become outdated, which affects the generalization performance of PLMs on future data.In this work, we propose TEMPOSUM, a novel benchmark that contains data samples from 2010 to 2022, to understand the temporal generalization ability of abstractive summarization models.Through extensive human evaluation, we show that parametric knowledge stored in summarization models significantly affects the faithfulness of the generated summaries on future data.Moreover, existing faithfulness enhancement methods cannot reliably improve the faithfulness of summarization models on future data.Finally, we discuss several recommendations to the research community on how to evaluate and improve the temporal generalization capability of text summarization models. 1

Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT).Existing transfer learning methods for NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization.In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model.Specifically, for each training instance of the child model, ConsistTL constructs the semantically-equivalent instance for the parent model and encourages prediction consistency between the parent and child for this instance, which is equivalent to the child model learning each instance under the guidance of the parent model.Experimental results on five low-resource NMT tasks demonstrate that ConsistTL results in significant improvements over strong transfer learning baselines, with a gain up to 1.7 BLEU over the existing backtranslation model on the widely-used WMT17 Turkish-English benchmark.Further analysis reveals that ConsistTL can improve the inference calibration of the child model.Code and scripts are freely available at https://github. com/NLP2CT/ConsistTL.