Papers by Jamie Callan
Modularized Transfomer-based Ranking Framework (2020.emnlp-main)
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| Challenge: | Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. |
| Approach: | They propose to modularize a Transformer ranker into separate modules for text representation and interaction. |
| Outcome: | The proposed model is faster than previous models and is easier to interpret and understand. |
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List (2021.naacl-main)
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| Challenge: | Recent neural IR models shift towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. |
| Approach: | They propose a contextualized exact match retrieval architecture where scoring is based on overlapping query document tokens’ contextualized representations. |
| Outcome: | The proposed architecture outperforms classical lexical retrieval systems and state-of-the-art deep language models with smaller latency. |
Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval (2024.acl-short)
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| Challenge: | Existing studies have shown that Transformer-based language models lose information in the middle of input sequences, especially in the context of web document retrieval. |
| Approach: | They examine position biases at multiple stages of the training pipeline for an encoder-decoder neural retrieval model, namely language model pre-training, contrastive pre- training, and contrastive fine-tuning. |
| Outcome: | The proposed model generates embeddings that better capture the beginning of the input content, with fine-tuning further aggravating this effect. |
Condenser: a Pre-training Architecture for Dense Retrieval (2021.emnlp-main)
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| Challenge: | Prior work fine-tunes deep LMs to encode text sequences into single dense vector representations, but dense encoders require a lot of data and sophisticated techniques to train and suffer in low data situations. |
| Approach: | They propose to pre-train Transformer language models (LMs) with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. |
| Outcome: | The proposed model improves on various text retrieval and similarity tasks by large margins over standard LMs. |
Making Information Seeking Easier: An Improved Pipeline for Conversational Search (2020.findings-emnlp)
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| Challenge: | Existing tools for conversational information seeking (CIS) do not support conversational contexts. |
| Approach: | They propose a highly effective pipeline for passage retrieval in a conversational search setting using a BERT-based classifier and a multi-view reranking component. |
| Outcome: | The proposed pipeline achieves 14.8% performance improvement over the current state-of-the-art pipeline and surpasses the Oracle. |
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval (2022.acl-long)
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| Challenge: | Recent research shows that fine-tuning dense retrievers to realize their capacity requires carefully designed fine-cuning techniques. |
| Approach: | They propose a pre-training architecture that learns to condense information into the dense vector through LM pre-training and a coCondenser architecture which adds an unsupervised corpus-level contrastive loss to warm up the passage embedding space. |
| Outcome: | The proposed architecture reduces the need for heavy data engineering and large batch training. |
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer (2022.emnlp-main)
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| Challenge: | eschewing separate architecture and training for knowledge-intensive tasks is cumbersome . end-to-end training only based on supervision from the end task is awkward . |
| Approach: | They propose a single Transformer that performs retrieval as attention and end-to-end training solely based on supervision from the end QA task. |
| Outcome: | The proposed model outperforms state-of-the-art retrievers and readers on in-domain datasets. |
Precise Zero-Shot Dense Retrieval without Relevance Labels (2023.acl-long)
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| Challenge: | Existing dense retrieval systems that use semantic embedding similarities can be effective across tasks and languages. |
| Approach: | They propose to pivot through Hypothetical Document Embeddings (HyDE) given a query, HyDE first zero-shot prompts an instruction-following language model to generate a hypothetical document. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across tasks and languages. |
Active Retrieval Augmented Generation (2023.emnlp-main)
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Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig
| Challenge: | Generative language models (LMs) have a tendency to hallucinate and create inaccurate output. |
| Approach: | They propose a method which iteratively uses a prediction of the upcoming sentence to anticipate future content. |
| Outcome: | The proposed method achieves superior or competitive performance on all tasks . iteratively uses a prediction of the upcoming sentence to anticipate future content . |