In this paper, we identify a new category of bias that induces
input-conflicting hallucinations, where large language models (LLMs) generate
responses inconsistent with the content of the input context. This issue we
have termed the false negative problem refers to the phenomenon where LLMs are
predisposed to return negative judgments when assessing the correctness of a
statement given the context. In experiments involving pairs of statements that
contain the same information but have contradictory factual directions, we
observe that LLMs exhibit a bias toward false negatives. Specifically, the
model presents greater overconfidence when responding with False. Furthermore,
we analyze the relationship between the false negative problem and context and
query rewriting and observe that both effectively tackle false negatives in
LLMs.

为了解决语言模型生成与输入上下文不一致的响应的偏见问题，本文揭示了一类新的偏见，即输入 - 冲突幻觉。通过实验证明，语言模型在评估语句的正确性时，更偏向于返回虚假的负面判断，表现出更强的过度自信，并研究了上下文和查询重写对于解决偏见问题的有效性。

大型语言模型是怀疑论者：输入冲突妄想的假阴性问题

Large Language Models are Skeptics: False Negative Problem of  Input-conflicting Hallucination

Relation extraction (RE) aims to extract relations from sentences and
documents. Existing relation extraction models typically rely on supervised
machine learning. However, recent studies showed that many RE datasets are
incompletely annotated. This is known as the false negative problem in which
valid relations are falsely annotated as 'no_relation'. Models trained with
such data inevitably make similar mistakes during the inference stage.
Self-training has been proven effective in alleviating the false negative
problem. However, traditional self-training is vulnerable to confirmation bias
and exhibits poor performance in minority classes. To overcome this limitation,
we proposed a novel class-adaptive re-sampling self-training framework.
Specifically, we re-sampled the pseudo-labels for each class by precision and
recall scores. Our re-sampling strategy favored the pseudo-labels of classes
with high precision and low recall, which improved the overall recall without
significantly compromising precision. We conducted experiments on
document-level and biomedical relation extraction datasets, and the results
showed that our proposed self-training framework consistently outperforms
existing competitive methods on the Re-DocRED and ChemDisgene datasets when the
training data are incompletely annotated. Our code is released at
this https URL

本文提出了一种新的自适应重新采样自训练框架，采用精度和召回率对每个类别的伪标签进行重新采样，以增加整体召回率而不过多牺牲精度，并在文件级别和生物医学关系提取数据集上进行实验表明，所提出的自训练框架在数据集的不完全注释时比现有方法表现要好。