We explore the use of Large Language Model (LLM-based) chatbots to power
recommender systems. We observe that the chatbots respond poorly when they
encounter under-specified requests (e.g., they make incorrect assumptions,
hedge with a long response, or refuse to answer). We conjecture that such
miscalibrated response tendencies (i.e., conversational priors) can be
attributed to LLM fine-tuning using annotators -- single-turn annotations may
not capture multi-turn conversation utility, and the annotators' preferences
may not even be representative of users interacting with a recommender system.
We first analyze public LLM chat logs to conclude that query
under-specification is common. Next, we study synthetic recommendation problems
with configurable latent item utilities and frame them as Partially Observed
Decision Processes (PODP). We find that pre-trained LLMs can be sub-optimal for
PODPs and derive better policies that clarify under-specified queries when
appropriate. Then, we re-calibrate LLMs by prompting them with learned control
messages to approximate the improved policy. Finally, we show empirically that
our lightweight learning approach effectively uses logged conversation data to
re-calibrate the response strategies of LLM-based chatbots for recommendation
tasks.

通过对聊天记录分析，发现查询的不明确性是常见的，据此，借助控制信息重校准预训练大型语言模型聊天机器人的回应策略，以改进其在推荐任务中的表现。

克服 LLM 聊天机器人中误校准的对话先验

On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots

Over the past few years, research has witnessed the advancement of deep
learning models trained on large datasets, some even encompassing millions of
examples. While these impressive performance on their hidden test sets, they
often underperform when assessed on external datasets. Recognizing the critical
role of generalization in medical AI development, many prestigious journals now
require reporting results both on the local hidden test set as well as on
external datasets before considering a study for publication. Effectively, the
field of medical AI has transitioned from the traditional usage of a single
dataset that is split into train and test to a more comprehensive framework
using multiple datasets, some of which are used for model development (source
domain) and others for testing (target domains). However, this new experimental
setting does not necessarily resolve the challenge of generalization. This is
because of the variability encountered in intended use and specificities across
hospital cultures making the idea of universally generalizable systems a myth.
On the other hand, the systematic, and a fortiori recurrent re-calibration, of
models at the individual hospital level, although ideal, may be overoptimistic
given the legal, regulatory and technical challenges that are involved.
Re-calibration using transfer learning may not even be possible in some
instances where reference labels of target domains are not available. In this
perspective we establish a hierarchical three-level scale system reflecting the
generalization level of a medical AI algorithm. This scale better reflects the
diversity of real-world medical scenarios per which target domain data for
re-calibration of models may or not be available and if it is, may or not have
reference labels systematically available.

医学人工智能算法的泛化水平可以通过建立一个分层三级评估系统来反映，该系统更好地反映了真实医疗情境的多样性，其中用于重新校准模型的目标领域数据可能可用也可能不可用，并且如果可用的话，可能或可能不会系统地提供参考标签。

医疗人工智能中的泛化问题：发展可扩展模型的视角

Generalization in medical AI: a perspective on developing scalable  models

We introduce distributed NLI, a new NLU task with a goal to predict the
distribution of human judgements for natural language inference. We show that
by applying additional distribution estimation methods, namely, Monte Carlo
(MC) Dropout, Deep Ensemble, Re-Calibration, and Distribution Distillation,
models can capture human judgement distribution more effectively than the
softmax baseline. We show that MC Dropout is able to achieve decent performance
without any distribution annotations while Re-Calibration can give further
improvements with extra distribution annotations, suggesting the value of
multiple annotations for one example in modeling the distribution of human
judgements. Despite these improvements, the best results are still far below
the estimated human upper-bound, indicating that predicting the distribution of
human judgements is still an open, challenging problem with a large room for
improvements. We showcase the common errors for MC Dropout and Re-Calibration.
Finally, we give guidelines on the usage of these methods with different levels
of data availability and encourage future work on modeling the human opinion
distribution for language reasoning. Our code and data are publicly available
at this https URL

介绍了分布式 NLI 任务，并探究了通过 Monte Carlo Dropout、Deep Ensemble、Re-Calibration 和 Distribution Distillation 等分布估计方法来更有效地捕捉人类判断分布的模型。说明了对一个示例进行多次注释对建模人类判断分布的价值，但最佳结果仍远低于人类的上限，最后支持特定情况下这些方法的使用并鼓励未来的工作。