One of the ways Large Language Models (LLMs) are used to perform machine
learning tasks is to provide them with a few examples before asking them to
produce a prediction. This is a meta-learning process known as few-shot
learning. In this paper, we use available Search-Based methods to optimise the
number and combination of examples that can improve an LLM's estimation
performance, when it is used to estimate story points for new agile tasks. Our
preliminary results show that our SBSE technique improves the estimation
performance of the LLM by 59.34% on average (in terms of mean absolute error of
the estimation) over three datasets against a zero-shot setting.

使用搜索引擎为基础的方法优化少样本学习过程，提高大型语言模型在敏捷任务中的估算性能。初步结果显示，我们的方法平均提高了 59.34% 的估算性能（以估算的平均绝对误差为指标），相较于零样本设置，针对三个数据集。

基于搜索的优化 LLM 学习技巧用于故事点估算

Search-based Optimisation of LLM Learning Shots for Story Point  Estimation

Despite the remarkable advances in language modeling, current mainstream
decoding methods still struggle to generate texts that align with human texts
across different aspects. In particular, sampling-based methods produce
less-repetitive texts which are often disjunctive in discourse, while
search-based methods maintain topic coherence at the cost of increased
repetition. Overall, these methods fall short in achieving holistic alignment
across a broad range of aspects. In this work, we frame decoding from a
language model as an optimization problem with the goal of strictly matching
the expected performance with human texts measured by multiple metrics of
desired aspects simultaneously. The resulting decoding distribution enjoys an
analytical solution that scales the input language model distribution via a
sequence-level energy function defined by these metrics. And most importantly,
we prove that this induced distribution is guaranteed to improve the perplexity
on human texts, which suggests a better approximation to the underlying
distribution of human texts. To facilitate tractable sampling from this
globally normalized distribution, we adopt the Sampling-Importance-Resampling
technique. Experiments on various domains and model scales demonstrate the
superiority of our method in metrics alignment with human texts and human
evaluation over strong baselines.

通过在语言模型中引入优化问题的框架，我们提出了一种新的解码分布，该分布通过序列级能量函数定义多个度量标准来改善与人类文本的语义一致性，实验证明我们的方法在与人类文本的度量标准一致性和人类评估方面优于强基准模型。

语言模型解码作为直接度量优化

Language Model Decoding as Direct Metrics Optimization

Search-based methods for hard combinatorial optimization are often guided by
heuristics. Tuning heuristics in various conditions and situations is often
time-consuming. In this paper, we propose NeuRewriter that learns a policy to
pick heuristics and rewrite the local components of the current solution to
iteratively improve it until convergence. The policy factorizes into a
region-picking and a rule-picking component, each parameterized by a neural
network trained with actor-critic methods in reinforcement learning.
NeuRewriter captures the general structure of combinatorial problems and shows
strong performance in three versatile tasks: expression simplification, online
job scheduling and vehicle routing problems. NeuRewriter outperforms the
expression simplification component in Z3; outperforms DeepRM and Google
OR-tools in online job scheduling; and outperforms recent neural baselines and
Google OR-tools in vehicle routing problems.

本论文提出了一种基于 NeuRewriter 的方法，该方法使用 actor-critic 强化学习来指导启发式搜索，并成功地解决了三个复杂的组合问题。