Where is the answer? An empirical study of positional bias for parametric knowledge extraction in language model (2025)

Abstract: Language model (LM) stores diverse factual knowledge in their parameters, which is learned during self-supervised training on unlabeled documents and is made extractable by instruction-tuning. For knowledge-intensive tasks, it is essential to memorize information in a way that makes it extractable from LM’s parameters with diverse queries. However, LMs suffer from a phenomenon called “perplexity curse”; despite minimizing document perplexity during training, LMs struggle to extract information via a question prompt. In this paper, we study the problem by fine-tuning LMs for new data and find a very intriguing fact that all studied LMs suffer from positional bias in the training document, i.e., they struggle to answer questions about the information described in the middle or at the end of the training document. Our study indicates that this problem stems from the auto-regressive training, ie., predicting the next token given all previous tokens, thus adding regularization mitigates the issue. Our discoveries supported by extensive analysis will be an important key to extracting knowledge from the parameters of LMs. We will publish our code and dataset upon acceptance.

## introduction language model stores diverse factual knowledge in their parameters, which is learned during selfsupervised training on a huge amount of documents and is made extractable through question answering by instruction-tuning @xcite @xcite @xcite @xcite . ## dataset we introduce two datasets to analyze the positional bias issue. we focus on whether lm can answer questions about factual knowledge learned from training documents in a closed-qa setting. we leave most details for the appendix due to the limited space. first, we generate the synthetic dataset in a controlled manner. second, we introduce a real dataset collected from the articles of wikipedia, following @xcite , to study llm's ability to memorize information in real documents. our qa data is annotated with the corresponding source sentence from the documents, which enables us to analyze the impact from the position of the information. see sec. b.1 for details. ## experiments section 4.1 describes the preliminary and sec. 4.2 explains the studied techniques. in sec. 4.3, we study the positional bias problem by controlling the position of the answer sentence in documents. in sec. 4.4, we focus on evaluating the model's performance on wiki2023+ to gain insight into how the studied recipes affect performance in a general qa setting. we summarize the observations as follows: ## conclusion in this work, we investigate the issue of "perplexity curse" in the continued training of llm. then, we find a very intriguing fact that llms struggle to extract information beyond the first sentence. our study indicates that auto-regressive training forces the model to memorize contents relying on many irrelevant tokens, and simple and easy techniques such as denoising auto-regressive training mitigate the issue. our published code and dataset should encourage more researchers to investigate the issue.