Large Language Models (LLMs) have been revolutionizing a myriad of natural
language processing tasks with their diverse zero-shot capabilities. Indeed,
existing work has shown that LLMs can be used to great effect for many tasks,
such as information retrieval (IR), and passage ranking. However, current
state-of-the-art results heavily lean on the capabilities of the LLM being
used. Currently, proprietary, and very large LLMs such as GPT-4 are the highest
performing passage re-rankers. Hence, users without the resources to leverage
top of the line LLMs, or ones that are closed source, are at a disadvantage. In
this paper, we investigate the use of a pre-filtering step before passage
re-ranking in IR. Our experiments show that by using a small number of human
generated relevance scores, coupled with LLM relevance scoring, it is
effectively possible to filter out irrelevant passages before re-ranking. Our
experiments also show that this pre-filtering then allows the LLM to perform
significantly better at the re-ranking task. Indeed, our results show that
smaller models such as Mixtral can become competitive with much larger
proprietary models (e.g., ChatGPT and GPT-4).

使用人工生成的相关性评分和 LLM 相关性评分，在对信息检索进行重新排序之前进行预过滤，能够有效地过滤掉不相关的段落，并使较小模型如 Mixtral 能够与更大的专有模型（如 ChatGPT 和 GPT-4）具有竞争力。

逐步重新排序：研究大型语言模型的预过滤重新排序

Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with  Large Language Models

Graph coloring is a problem with varied applications in industry and science
such as scheduling, resource allocation, and circuit design. The purpose of
this paper is to establish if a new gradient based iterative solver framework
known as heat diffusion can solve the graph coloring problem. We propose a
solution to the graph coloring problem using the heat diffusion framework. We
compare the solutions against popular methods and establish the competitiveness
of heat diffusion method for the graph coloring problem.

本文旨在使用热扩散框架解决图着色问题，并与传统方法进行比较，证明热扩散方法在图着色问题中的竞争力。

利用热扩散进行图着色

Graph Coloring Using Heat Diffusion

Identifying public misinformation is a complicated and challenging task. An
important part of checking the veracity of a specific claim is to evaluate the
stance different news sources take towards the assertion. Automatic stance
evaluation, i.e. stance detection, would arguably facilitate the process of
fact checking. In this paper, we present our stance detection system which
claimed third place in Stage 1 of the Fake News Challenge. Despite our
straightforward approach, our system performs at a competitive level with the
complex ensembles of the top two winning teams. We therefore propose our system
as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance
detection task.

该论文介绍了一个能够识别公众错误信息、帮助事实核查的简单实用的立场判断系统，它在 Fake News Challenge 的第一阶段中获得第三名，与前两名获胜队伍的复杂集成方法相比表现竞争力，作者将其提议为 Fake News Challenge 立场检测任务的简单但强大的基线。

Fake News Challenge 立场检测任务的简单而强大的基准线

A simple but tough-to-beat baseline for the Fake News Challenge stance  detection task

Predicting the outcome of sports events is a hard task. We quantify this
difficulty with a coefficient that measures the distance between the observed
final results of sports leagues and idealized perfectly balanced competitions
in terms of skill. This indicates the relative presence of luck and skill. We
collected and analyzed all games from 198 sports leagues comprising 1503
seasons from 84 countries of 4 different sports: basketball, soccer, volleyball
and handball. We measured the competitiveness by countries and sports. We also
identify in each season which teams, if removed from its league, result in a
completely random tournament. Surprisingly, not many of them are needed. As
another contribution of this paper, we propose a probabilistic graphical model
to learn about the teams' skills and to decompose the relative weights of luck
and skill in each game. We break down the skill component into factors
associated with the teams' characteristics. The model also allows to estimate
as 0.36 the probability that an underdog team wins in the NBA league, with a
home advantage adding 0.09 to this probability. As shown in the first part of
the paper, luck is substantially present even in the most competitive
championships, which partially explains why sophisticated and complex
feature-based models hardly beat simple models in the task of forecasting
sports' outcomes.

本文提出了一个通过计算比赛结果与完美平衡比赛结果之间距离的系数来衡量体育赛事预测的难度，并通过收集和分析 198 个体育联赛中的所有比赛来衡量竞争力与各国和各项运动的竞争力。文章还提出了概率图模型来学习团队技能，并将技能分解为与团队特征相关的因素，并估计大约 36％的概率，预测出在 NBA 联赛中，劣势团队获胜，这部分解释了为什么复杂的特征模型很难战胜简单的模型。