Task: Summarization / N-line summary
User instruction:
Based on the following paper's Introduction, summarize its key points in no more than five sentences in Korean.
 
Recent advancements in large language models (LLMs) have enabled complex reasoning tasks executed by understanding user instructions (Ouyang et al., 2022; Touvron et al., 2023; Achiam et al., 2023; Jiang et al., 2023). As the capabilities of LLMs have expanded rapidly, research has intensified to analyze their characteristics and inherent issues. One of the major issues is the generation of factually incorrect content, known as “hallucination”, which significantly degrades the reliability of LLM-based services (Zhang et al., 2023; Xu et al., 2024; Huang et al., 2023). The hallucination problem in LLMs can be attributed to factors such as parametric knowledge, overconfidence, and biases (Zhang et al., 2023). Studies have been conducted to understand the decision-making mechanisms in LLMs from a parameter perspective. For instance, the “logit lens” technique allows for the interpretation of the model’s reasoning process along the layers, exploiting the hidden representations from intermediate layers (Belrose et al., 2023; Ferrando et al., 2023). This technique has been utilized to analyze model characteristics in various scenarios such as in-context learning. On the other hand, Yuan et al. (2024) focus on analyzing hallucination phenomenon based on attention heads. However, the causes and mechanisms of hallucination in LLMs vary by type of task, indicating that extensive further study is still necessary. In this paper, we aim to identify biases that emerge when LLMs respond to questions requiring a binary decision such as affirmation or negation. Specifically, in yes-no question-answering
(QA) tasks that demand complex reasoning, such as mathematical or logical reasoning, we observe a negative bias in LLMs. This negative bias, which implies a discrepancy between the model’s reasoning ability and accuracy, potentially decreases the reliability of model predictions. From the rationale that the model allocates higher attention to the negative candidates among the binary answer options presented in the user prompt, we propose a negative attention score (NAS) to systematically probe these negatively biased attention heads in large language models (LLMs). Based on NAS, we detect attention heads that attend to tokens associated with negation in an instruction. Our findings also confirm the existence of attention heads that are predominantly involved in manifesting this negative bias.
To address the identified attention heads, we introduce a parameter-efficient fine-tuning technique named NAS alignment (NASA). In NASA, the model is incrementally fine-tuned, starting with the most biased attention head. The method includes periodic monitoring of NAS which is used for automatic scheduling of early stopping and update cancellation. Experimental results demonstrate that our method significantly reduces negative bias while
maintaining general reasoning performance. Additionally, we observe an improvement in calibration for yes-no QA tasks in LLMs.

Evaluation criteria:
The summary must be written in Korean.
The summary should be no more than five sentences.
The summary must not distort the content or add information not provided.
The summary must include the main points from the paper’s Introduction (e.g., causes of hallucinations, analysis of negative bias, the proposed NAS, the NASA technique, etc.).