Sarcasm recognition is challenging because it needs an understanding of the
true intention, which is opposite to or different from the literal meaning of
the words. Prior work has addressed this challenge by developing a series of
methods that provide richer $contexts$, e.g., sentiment or cultural nuances, to
models. While shown to be effective individually, no study has systematically
evaluated their collective effectiveness. As a result, it remains unclear to
what extent additional contexts can improve sarcasm recognition. In this work,
we explore the improvements that existing methods bring by incorporating more
contexts into a model. To this end, we develop a framework where we can
integrate multiple contextual cues and test different approaches. In evaluation
with four approaches on three sarcasm recognition benchmarks, we achieve
existing state-of-the-art performances and also demonstrate the benefits of
sequentially adding more contexts. We also identify inherent drawbacks of using
more contexts, highlighting that in the pursuit of even better results, the
model may need to adopt societal biases.

通过整合多种上下文，本研究探索了现有方法在讽刺识别中的改进，并在三个讽刺识别基准测试中取得了最先进的性能，并展示了添加更多上下文的好处，同时也指出使用更多上下文可能引入社会偏见的固有缺点。

何时使用 “更多语境” 有助于辨别嘲讽？

When Do "More Contexts" Help with Sarcasm Recognition?

As large language models (LLMs) take on more complex tasks, their inputs
incorporate longer contexts to respond to questions that require domain
knowledge or user-specific conversational histories. Yet, using long contexts
poses a challenge for responsive LLM systems, as nothing can be generated until
all the contexts are fetched to and processed by the LLM. Existing systems
optimize only the computation delay in context processing (e.g., by caching
intermediate key-value features of the text context) but often cause longer
network delays in context fetching (e.g., key-value features consume orders of
magnitude larger bandwidth than the text context).
This paper presents CacheGen to minimize the delays in fetching and
processing contexts for LLMs. CacheGen reduces the bandwidth needed for
transmitting long contexts' key-value (KV) features through a novel encoder
that compresses KV features into more compact bitstream representations. The
encoder combines adaptive quantization with a tailored arithmetic coder, taking
advantage of the KV features' distributional properties, such as locality
across tokens. Furthermore, CacheGen minimizes the total delay in fetching and
processing a context by using a controller that determines when to load the
context as compressed KV features or raw text and picks the appropriate
compression level if loaded as KV features. We test CacheGen on three models of
various sizes and three datasets of different context lengths. Compared to
recent methods that handle long contexts, CacheGen reduces bandwidth usage by
3.7-4.3x and the total delay in fetching and processing contexts by 2.7-3x
while maintaining similar LLM performance on various tasks as loading the text
contexts.

CacheGen 通过将上下文的键值（KV）特征压缩成更紧凑的比特流表示形式，从而减少获取和处理上下文的延迟，并降低带宽使用量。在测试中，相对于处理长上下文的最近方法，CacheGen 在保持类似的大型语言模型任务性能的同时，减少了带宽使用量 3.7-4.3 倍，减少了获取和处理上下文的总延迟 2.7-3 倍。

CacheGen：面向语言模型应用的快速上下文加载

CacheGen: Fast Context Loading for Language Model Applications

Generating inferential texts about an event in different perspectives
requires reasoning over different contexts that the event occurs. Existing
works usually ignore the context that is not explicitly provided, resulting in
a context-independent semantic representation that struggles to support the
generation. To address this, we propose an approach that automatically finds
evidence for an event from a large text corpus, and leverages the evidence to
guide the generation of inferential texts. Our approach works in an
encoder-decoder manner and is equipped with a Vector Quantised-Variational
Autoencoder, where the encoder outputs representations from a distribution over
discrete variables. Such discrete representations enable automatically
selecting relevant evidence, which not only facilitates evidence-aware
generation, but also provides a natural way to uncover rationales behind the
generation. Our approach provides state-of-the-art performance on both
Event2Mind and ATOMIC datasets. More importantly, we find that with discrete
representations, our model selectively uses evidence to generate different
inferential texts.

研究使用自然语言处理技术，提出了一个基于编码器 - 解码器和自动查找语料库中事件证据的半监督方法，提高了生成事件推理性文本的性能。该方法在 Event2Mind 和 ATOMIC 数据集上达到了最先进的水平。