Table-to-text generation, a long-standing challenge in natural language
generation, has remained unexplored through the lens of subjectivity.
Subjectivity here encompasses the comprehension of information derived from the
table that cannot be described solely by objective data. Given the absence of
pre-existing datasets, we introduce the Ta2TS dataset with 3849 data instances.
We perform the task of fine-tuning sequence-to-sequence models on the
linearized tables and prompting on popular large language models. We analyze
the results from a quantitative and qualitative perspective to ensure the
capture of subjectivity and factual consistency. The analysis shows the
fine-tuned LMs can perform close to the prompted LLMs. Both the models can
capture the tabular data, generating texts with 85.15% BERTScore and 26.28%
Meteor score. To the best of our knowledge, we provide the first-of-its-kind
dataset on tables with multiple genres and subjectivity included and present
the first comprehensive analysis and comparison of different LLM performances
on this task.

通过对线性化表格进行序列到序列模型的微调和在流行的大型语言模型上进行提示，我们分析了从定量和定性的角度来确保捕捉主观性和事实的一致性的结果，显示出微调的 LMs 可以接近提示的 LLMs。根据我们的了解，我们提供了一种多类型和包含主观性的表格数据集，并对不同的 LLM 性能进行了首次全面的分析和比较。

事实与感受：在表格转文本生成中捕捉客观性和主观性

Facts-and-Feelings: Capturing both Objectivity and Subjectivity in  Table-to-Text Generation

Although contemporary large language models (LMs) demonstrate impressive
question-answering capabilities, their answers are typically the product of a
single call to the model. This entails an unwelcome degree of opacity and
compromises performance, especially on problems that are inherently multi-step.
To address these limitations, we show how LMs can be made to perform faithful
multi-step reasoning via a process whose causal structure mirrors the
underlying logical structure of the problem. Our approach works by chaining
together reasoning steps, where each step results from calls to two fine-tuned
LMs, one for selection and one for inference, to produce a valid reasoning
trace. Our method carries out a beam search through the space of reasoning
traces to improve reasoning quality. We demonstrate the effectiveness of our
model on multi-step logical deduction and scientific question-answering,
showing that it outperforms baselines on final answer accuracy, and generates
humanly interpretable reasoning traces whose validity can be checked by the
user.

通过串联使用微调语言模型进行多步推理的方法，可以解决大型语言模型的单次调用限制，提高多步问题的性能和可解释性。该方法在多步逻辑推导和科学问题回答方面表现优于基线模型，并生成可由用户检查的有效推理过程。

利用大型语言模型进行忠实推理

Faithful Reasoning Using Large Language Models

There are concerns that the ability of language models (LMs) to generate high
quality synthetic text can be misused to launch spam, disinformation, or
propaganda. Therefore, the research community is actively working on developing
approaches to detect whether a given text is organic or synthetic. While this
is a useful first step, it is important to be able to further fingerprint the
author LM to attribute its origin. Prior work on fingerprinting LMs is limited
to attributing synthetic text generated by a handful (usually < 10) of
pre-trained LMs. However, LMs such as GPT2 are commonly fine-tuned in a myriad
of ways (e.g., on a domain-specific text corpus) before being used to generate
synthetic text. It is challenging to fingerprinting fine-tuned LMs because the
universe of fine-tuned LMs is much larger in realistic scenarios. To address
this challenge, we study the problem of large-scale fingerprinting of
fine-tuned LMs in the wild. Using a real-world dataset of synthetic text
generated by 108 different fine-tuned LMs, we conduct comprehensive experiments
to demonstrate the limitations of existing fingerprinting approaches. Our
results show that fine-tuning itself is the most effective in attributing the
synthetic text generated by fine-tuned LMs.

研究社区正在积极开发检测给定文本是有机的还是合成的方法，本文则聚焦于探讨如何对精细调整的语言模型进行指纹识别以确定其来源，并通过实验结果表明，精细调整本身是确定合成文本来源的最有效方法。