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Lexical exact-match systems perform text retrieval efficiently with sparse matching signals and fast retrieval through inverted lists, but naturally suffer from the mismatch between lexical surface form and implicit term semantics. This paper proposes to directly bridge the surface form space and the term semantics space in lexical exact-match retrieval via contextualized surface forms (CSF). Each CSF pairs a lexical surface form with a context source, and is represented by a lexical form weight and a contextualized semantic vector representation. This framework is able to perform sparse lexicon-based retrieval by learning to represent each query and document as a "bag-of-CSFs", simultaneously addressing two key factors in sparse retrieval: vocabulary expansion of surface form and semantic representation of term meaning. At retrieval time, it efficiently matches CSFs through exact-match of learned surface forms, and effectively scores each CSF pair via contextual semantic representations, leading to joint improvement in both term match and term scoring. Multiple experiments show that this approach successfully resolves the main mismatch issues in lexical exact-match retrieval and outperforms state-of-the-art lexical exact-match systems, reaching comparable accuracy as lexical all-to-all soft match systems as an efficient exact-match-based system. 

Recent rapid advances in deep pre-trained language models and the introduction of large datasets have powered research in embedding-based neural retrieval. While many excellent research papers have emerged, most of them come with their own implementations, which are typically optimized for some particular research goals instead of efficiency or code organization. In this paper, we introduce Tevatron, a neural retrieval toolkit that is optimized for efficiency, flexibility, and code simplicity. Tevatron enables model training and evaluation for a variety of ranking components such as dense retrievers, sparse retrievers, and rerankers. It also provides a standardized pipeline that includes text processing, model training, corpus/query encoding, and search. In addition, Tevatron incorporates well-studied methods for improving retriever effectiveness such as hard negative mining and knowledge distillation. We provide an overview of Tevatron in this paper, demonstrating its effectiveness and efficiency on multiple IR and QA datasets. We highlight Tevatron’s flexible design, which enables easy generalization across datasets, model architectures, and accelerator platforms (GPUs and TPUs). Overall, we believe that Tevatron can serve as a solid software foundation for research on neural retrieval systems, including their design, modeling, and optimization. 

Large language models (LLMs) have demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("few-shot prompting"). Much of this success can be attributed to prompting methods such as "chain-of-thought", which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LLMs seem to be adept at this sort of step-by-step decomposition, LLMs often make logical and arithmetic mistakes in the solution part, even when the problem is decomposed correctly. In this paper, we present Program-Aided Language models (PAL): a novel approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter. We demonstrate this synergy between a neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and algorithmic reasoning tasks from BIG-Bench Hard and others. In all these natural language reasoning tasks, generating code using an LLM and reasoning using a Python interpreter leads to more accurate results than much larger models. For example, PAL using Codex achieves state-of-the-art few-shot accuracy on GSM8K, surpassing PaLM which uses chain-of-thought by absolute 15% top-1. 

Lexical exact match systems that use inverted lists are a fundamental text retrieval architecture. A recent advance in neural IR, COIL, extends this approach with contextualized inverted lists from a deep language model backbone and performs retrieval by comparing contextualized query-document term representation, which is effective but computationally expensive. This paper explores the effectiveness-efficiency tradeoff in COIL-style systems, aiming to reduce the computational complexity of retrieval while preserving term semantics. It proposes COILcr, which explicitly factorizes COIL into intra-context term importance weights and cross-context semantic representations. At indexing time, COILcr further maps term semantic representations to a smaller set of canonical representations. Experiments demonstrate that canonical representations can efficiently preserve term semantics, reducing the storage and computational cost of COIL-based retrieval while maintaining model performance. The paper also discusses and compares multiple heuristics for canonical representation selection and looks into its performance in different retrieval settings. 

While dense retrieval has been shown to be effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance labels are available. In this paper, we recognize the difficulty of zero-shot learning and encoding relevance. Instead, we propose to pivot through Hypothetical Document Embeddings (HyDE). Given a query, HyDE first zero-shot prompts an instruction-following language model (e.g., InstructGPT) to generate a hypothetical document. The document captures relevance patterns but is “fake” and may contain hallucinations. Then, an unsupervised contrastively learned encoder (e.g., Contriever) encodes the document into an embedding vector. This vector identifies a neighborhood in the corpus embedding space, from which similar real documents are retrieved based on vector similarity. This second step grounds the generated document to the actual corpus, with the encoder’s dense bottleneck filtering out the hallucinations. Our experiments show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across various tasks (e.g. web search, QA, fact verification) and in non-English languages (e.g., sw, ko, ja, bn). 

Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunks. These chunks are independently mapped to scalar scores or latent vectors, which are then pooled into a final relevance score. These encode-and-pool methods however inevitably introduce an information bottleneck: the low dimension representations. In this paper, we propose instead to model full query-to-document interaction, leveraging the attention operation and modular Transformer re-ranker framework. First, document chunks are encoded independently with an encoder module. An interaction module then encodes the query and performs joint attention from the query to all document chunk representations. We demonstrate that the model can use this new degree of freedom to aggregate important information from the entire document. Our experiments show that this design produces effective re-ranking on two classical IR collections Robust04 and ClueWeb09, and a large-scale supervised collection MS-MARCO document ranking. CCS CONCEPTS • Information systems 

Recent research demonstrates the effectiveness of using fine-tuned language models (LM) for dense retrieval. However, dense retrievers are hard to train, typically requiring heavily engineered fine-tuning pipelines to realize their full potential. In this paper, we identify and address two underlying problems of dense retrievers: i) fragility to training data noise and ii) requiring large batches to robustly learn the embedding space. We use the recently proposed Condenser pre-training architecture, which learns to condense information into the dense vector through LM pre-training. On top of it, we propose coCondenser, which adds an unsupervised corpus-level contrastive loss to warm up the passage embedding space. Experiments on MS-MARCO, Natural Question, and Trivia QA datasets show that coCondenser removes the need for heavy data engineering such as augmentation, synthesis, or filtering, and the need for large batch training. It shows comparable performance to RocketQA, a state-of-the-art, heavily engineered system, using simple small batch fine-tuning. 

Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents. This is usually done through two separate models, a retriever that encodes the query and finds nearest neighbors, and a reader based on Transformers. These two components are usually modeled separately, which necessitates a cumbersome implementation and is awkward to optimize in an end-to-end fashion. In this paper, we revisit this design and eschew the separate architecture and training in favor of a single Transformer that performs retrieval as attention (RAA), and end-to-end training solely based on supervision from the end QA task. We demonstrate for the first time that an end-to-end trained single Transformer can achieve both competitive retrieval and QA performance on in-domain datasets, matching or even slightly outperforming state-of-the-art dense retrievers and readers. Moreover, end-to-end adaptation of our model significantly boosts its performance on out-of-domain datasets in both supervised and unsupervised settings, making our model a simple and adaptable end-to-end solution for knowledge-intensive tasks. 

Classical information retrieval systems such asBM25 rely on exact lexical match and carryout search efficiently with inverted list index. Recent neural IR models shifts towards soft semantic matching all query document terms,but they lose the computation efficiency of exact match systems.This paper presents COIL, a contextualized exact match retrieval architecture that brings semantic lexical matching. COIL scoring is based on overlapping query document tokens’ contextualized representations. The new architecture stores contextualized token representations in inverted lists, bringing together the efficiency of exact match and the representation power of deep language models. Our experimental results show COIL outperforms classical lexical retrievers and state-of-the-art deep LM retrievers with similar or smaller latency. 

Pre-trained deep language models (LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextualized matching signals. Meanwhile, deep LMs can also be leveraged to improve search index, building retrievers with better recall. One would expect a straightforward combination of both in a pipeline to have additive performance gain. In this paper, we discover otherwise and that popular reranker cannot fully exploit the improved retrieval result. We, therefore, propose a Localized Contrastive Estimation (LCE) for training rerankers and demonstrate it significantly improves deep two-stage models (Our codes are open sourced at https://github.com/luyug/Reranker.).