GitHub issue resolving recently has attracted significant attention from
academia and industry. SWE-bench is proposed to measure the performance in
resolving issues. In this paper, we propose CodeR, which adopts a multi-agent
framework and pre-defined task graphs to Repair & Resolve reported bugs and add
new features within code Repository. On SWE-bench lite, CodeR is able to solve
28.00% of issues, in the case of submitting only once for each issue. We
examine the performance impact of each design of CodeR and offer insights to
advance this research direction.

采用多智能体框架和预定义的任务图，提出了 CodeR，用于修复和解决代码存储库中报告的错误和添加新功能。在 SWE-bench lite 中，CodeR 能够解决 28.00％的问题，每个问题仅需提交一次。检验了 CodeR 的每个设计对性能的影响，并提供了推进这个研究方向的见解。

代码 R：多智能体和任务图的问题解决

CodeR: Issue Resolving with Multi-Agent and Task Graphs

Contrastive learning has been the dominant approach to training dense
retrieval models. In this work, we investigate the impact of ranking context -
an often overlooked aspect of learning dense retrieval models. In particular,
we examine the effect of its constituent parts: jointly scoring a large number
of negatives per query, using retrieved (query-specific) instead of random
negatives, and a fully list-wise loss. To incorporate these factors into
training, we introduce Contextual Document Embedding Reranking (CODER), a
highly efficient retrieval framework. When reranking, it incurs only a
negligible computational overhead on top of a first-stage method at run time
(delay per query in the order of milliseconds), allowing it to be easily
combined with any state-of-the-art dual encoder method. After fine-tuning
through CODER, which is a lightweight and fast process, models can also be used
as stand-alone retrievers. Evaluating CODER in a large set of experiments on
the MS~MARCO and TripClick collections, we show that the contextual reranking
of precomputed document embeddings leads to a significant improvement in
retrieval performance. This improvement becomes even more pronounced when more
relevance information per query is available, shown in the TripClick
collection, where we establish new state-of-the-art results by a large margin.

通过引入 Contextual Document Embedding Reranking (CODER)，可以显著提高基于对比学习的密集检索模型的检索性能，特别是当更多的相关信息可用时，如在 TripClick 集合中观察到的情况下，可以获得大幅度的改进并取得新的最优结果。

CODER: 通过上下文文档嵌入重新排序的有效优化框架

CODER: An efficient framework for improving retrieval through COntextual  Document Embedding Reranking

This paper proposes CODER: contrastive learning on knowledge graphs for
cross-lingual medical term representation. CODER is designed for medical term
normalization by providing close vector representations for different terms
that represent the same or similar medical concepts with cross-lingual support.
We train CODER via contrastive learning on a medical knowledge graph (KG) named
the Unified Medical Language System, where similarities are calculated
utilizing both terms and relation triplets from KG. Training with relations
injects medical knowledge into embeddings and aims to provide potentially
better machine learning features. We evaluate CODER in zero-shot term
normalization, semantic similarity, and relation classification benchmarks,
which show that CODERoutperforms various state-of-the-art biomedical word
embedding, concept embeddings, and contextual embeddings. Our codes and models
are available at this https URL

该研究提出了 CODER：针对跨语言医学术语表现的知识图谱对比学习。CODER 通过对 Unified Medical Language System 进行对比学习进行训练，通过利用词条和关系三元组之间的相似性将医学知识注入到嵌入中，并旨在提供潜在的更好的机器学习特性，评估结果表明 CODER 在零样本术语归一化，语义相似性和关系分类基准方面优于各种最先进的生物医学词嵌入，概念嵌入和上下文嵌入。