Dense retrieval models are commonly used in Information Retrieval (IR) applications, such as Retrieval-Augmented Generation (RAG). Since they often serve as the first step in these systems, their robustness is critical to avoid downstream failures. In this work, we repurpose a relation extraction dataset (e.g., Re-DocRED) to design controlled experiments that quantify the impact of heuristic biases, such as a preference for shorter documents, on retrievers like Dragon+ and Contriever. We uncover major vulnerabilities, showing retrievers favor shorter documents, early positions, repeated entities, and literal matches, all while ignoring the answer’s presence! Notably, when multiple biases combine, models exhibit catastrophic performance degradation, selecting the answer-containing document in less than 10% of cases over a synthetic biased document without the answer. Furthermore, we show that these biases have direct consequences for downstream applications like RAG, where retrieval-preferred documents can mislead LLMs, resulting in a 34% performance drop than providing no documents at all.https://huggingface.co/datasets/mohsenfayyaz/ColDeR

Retrieval systems are central to many NLP pipelines, but often rely on surface-level cues such as keyword overlap and lexical semantic similarity.To evaluate retrieval beyond these shallow signals, recent benchmarks introduce reasoning-heavy queries; however, they primarily shift the burden to query-side processing techniques -like prompting or multi-hop retrieval -that can help resolve complexity.In contrast, we present IMPLIRET, a benchmark that shifts the reasoning challenge to documentside processing: The queries are simple, but relevance depends on facts stated implicitly in documents through temporal (e.g., resolving "two days ago"), arithmetic, and world knowledge relationships.We evaluate a range of sparse and dense retrievers, all of which struggle in this setting: the best nDCG@10 is only 14.91%.We also test whether long-context models can overcome this limitation.But even with a short context of only thirty documents, including the positive document, GPT-o4-mini scores only 55.54%, showing that document-side reasoning remains a challenge.Our codes are available at github.com/ZeinabTaghavi/IMPLIRET.

Recent large language models (LLMs) support long contexts ranging from 128K to 1M tokens. A popular method for evaluating these capabilities is the needle-in-a-haystack (NIAH) test, which involves retrieving a "needle" (relevant information) from a "haystack" (long irrelevant context). Extensions of this approach include increasing distractors, fact chaining, and in-context reasoning. However, in these benchmarks, models can exploit existing literal matches between the needle and haystack to simplify the task. To address this, we introduce NoLiMa, a benchmark extending NIAH with a carefully designed needle set, where questions and needles have minimal lexical overlap, requiring models to infer latent associations to locate the needle within the haystack. We evaluate 13 popular LLMs that claim to support contexts of at least 128K tokens. While they perform well in short contexts ($<$1K), performance degrades significantly as context length increases. At 32K, for instance, 11 models drop below 50% of their strong short-length baselines. Even GPT-4o, one of the top-performing exceptions, experiences a reduction from an almost-perfect baseline of 99.3% to 69.7%. Our analysis suggests these declines stem from the increased difficulty the attention mechanism faces in longer contexts when literal matches are absent, making it harder to retrieve relevant information. Even models enhanced with reasoning capabilities or CoT prompting struggle to maintain performance in long contexts. We publicly release the dataset and evaluation code at https://github.com/adobe-research/NoLiMa.

Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Several proposals have been put forward in recent years for improving out-of-distribution (OOD) performance through mitigating dataset biases.A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model.Here, the underlying assumption is that the biased model resorts to shortcut features.Hence, those training examples that are correctly predicted by the biased model are flagged as being biased and are down-weighted during the training of the main model.However, assessing the importance of an instance merely based on the predictions of the biased model may be too naive.It is possible that the prediction of the main model can be derived from another decisionmaking process that is distinct from the behavior of the biased model.To circumvent this, we introduce a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts (PoE) loss function to further improve OOD performance.With experiments conducted on natural language inference and fact verification benchmarks, we show that our method improves OOD results while maintaining in-distribution (ID) performance.1 ⋆ Work done as a Master's student at Iran University of Science and

Pre-trained language models have shown stellar performance in various downstream tasks.But, this usually comes at the cost of high latency and computation, hindering their usage in resource-limited settings.In this work, we propose a novel approach for reducing the computational cost of BERT with minimal loss in downstream performance.Our method dynamically eliminates less contributing tokens through layers, resulting in shorter lengths and consequently lower computational cost.To determine the importance of each token representation, we train a Contribution Predictor for each layer using a gradient-based saliency method.Our experiments on several diverse classification tasks show speedups up to 22x during inference time without much sacrifice in performance.We also validate the quality of the selected tokens in our method using human annotations in the ERASER benchmark.In comparison to other widely used strategies for selecting important tokens, such as saliency and attention, our proposed method has a significantly lower false positive rate in generating rationales.Our code is freely available at https://github.com/amodaresi/ AdapLeR.

Ali Modarressi, Mohsen Fayyaz, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Several studies have been carried out on revealing linguistic features captured by BERT.This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of BERT.The subsequent classification accuracy is then interpreted as the ability of the model in encoding the corresponding linguistic property.Despite providing insights, these studies have left out the potential role of token representations.In this paper, we provide a more in-depth analysis on the representation space of BERT in search for distinct and meaningful subspaces that can explain the reasons behind these probing results.Based on a set of probing tasks and with the help of attribution methods we show that BERT tends to encode meaningful knowledge in specific token representations (which are often ignored in standard classification setups), allowing the model to detect syntactic and semantic abnormalities, and to distinctively separate grammatical number and tense subspaces.1