@inproceedings{boytsov-etal-2025-positional,
    title = "Positional Bias in Long-Document Ranking: Impact, Assessment, and Mitigation",
    author = "Boytsov, Leonid  and
      Akinpelu, David  and
      Katyal, Nipun  and
      Lin, Tianyi  and
      Gao, Fangwei  and
      Zhao, Yutian  and
      Huang, Jeffrey  and
      Nyberg, Eric",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.46/",
    pages = "824--856",
    ISBN = "979-8-89176-298-5",
    abstract = "We tested over 20 Transformer models for ranking long documents (including recent LongP models trained with FlashAttention and RankGPT models ``powered'' by OpenAI and Anthropic cloud APIs).We compared them with the simple FirstP baseline, which applied the same model to truncated input (up to 512 tokens).On MS MARCO, TREC DL, and Robust04 no long-document model outperformed FirstP by more than 5{\%} (on average).We hypothesized that this lack of improvement is not due to inherent model limitations,but due to benchmark positional bias (most relevant passages tend to occur early in documents),which is known to exist in MS MARCO.To confirm this, we analyzed positional relevance distributions across four long-document corpora (with six query sets) and observed the same early-position bias.Surprisingly, we also found bias in six BEIR collections, which are typically categorized asshort-document datasets.We then introduced a new diagnostic dataset, MS MARCO FarRelevant, where relevant spans were deliberately placed beyond the first 512 tokens.On this dataset, many long-context models{---}including RankGPT{---}performed at random-baseline level, suggesting overfitting to positional bias.We also experimented with debiasing training data, but with limited success.Our findings (1) highlight the need for careful benchmark design in evaluating long-context models for document ranking, (2) identify model types that are more robust to positional bias, and (3) motivate further work on approaches to debias training data.We release our code and data to support further research."
}