The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and preferences. Retrieval augmentation emerges as an effective strategy, as it can accommodate a vast number of users without the costs from fine-tuning. Existing research, however, has largely focused on enhancing the retrieval stage and devoted limited exploration toward optimizing the representation of the database, a crucial aspect for tasks such as personalization. In this work, we examine the problem from a novel angle, focusing on how data can be better represented for more data-efficient retrieval in the context of LLM customization. To tackle this challenge, we introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts and collaborative refinement to effectively bridge knowledge gaps among users. In the evaluation of response prediction, Persona-DB demonstrates superior context efficiency in maintaining accuracy with a significantly reduced retrieval size, a critical advantage in scenarios with extensive histories or limited context windows. Our experiments also indicate a marked improvement of over 10% under cold-start scenarios, when users have extremely sparse data. Furthermore, our analysis reveals the increasing importance of collaborative knowledge as the retrieval capacity expands.

In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, embeds these images, and computes an input image–weighted query which is used to detect the desired concept in the image. Our proposed method is simple and training-free, yet achieves over 16.81% mAP improvement on ODinW and 59.85% mAP improvement on LVIS compared to state-of-the-art models such as GroundingDINO. We further show that our approach of basing object detection on a set of Web-retrieved exemplars is stable with respect to variations in the exemplars, suggesting a path towards eliminating costly data annotation and training procedures.1

Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and software maintenance, particularly as software systems increase in complexity. While current code embedding models have demonstrated promise in retrieving code snippets for small-scale, well-defined tasks, they often underperform in more demanding real-world applications such as bug localization within GitHub repositories. We hypothesize that a key issue is their reliance on noisy and inconsistent datasets for training, which impedes their ability to generalize to more complex retrieval scenarios. To address these limitations, we introduce CoRNStack, a large-scale, high-quality contrastive training dataset for code that spans multiple programming languages. This dataset is curated using consistency filtering to eliminate noisy positives and is further enriched with mined hard negatives, thereby facilitating more effective learning. We demonstrate that contrastive training of embedding models using CoRNStack leads to state-of-the-art performance across a variety of code retrieval tasks. Furthermore, the dataset can be leveraged for training code reranking models, a largely underexplored area compared to text reranking. Our finetuned code reranking model significantly improves the ranking quality over the retrieved results. Finally, by employing our code retriever and reranker together, we demonstrate significant improvements in function localization for GitHub issues, an important component of real-world software development.

Neural IR systems often employ a retrieve-and-rerank framework: a bi-encoder retrieves a fixed number of candidates (e.g., 𝐾=100), which a cross-encoder then reranks.Recent studies have indicated that relevance feedback from the reranker at inference time can improve the recall of the retriever.The approach works by updating the retriever's query representations via a distillation process that aligns it with the reranker's predictions.While a powerful idea, the arguably narrow scope of past studies focusing on a small number of specific domains such as english question answering and entity retrieval has left a gap in our understanding of how well it generalizes.In this paper, we study inference-time reranker relevance feedback extensively across multiple retrieval domains, languages, and modalities, while also investigating aspects such as the performance and latency implications of the number of distillation updates and feedback candidates.

Ranking is a fundamental problem in search, however, existing ranking algorithms usually restrict the granularity of ranking to full passages or require a specific dense index for each desired level of granularity.Such lack of flexibility in granularity negatively affects many applications that can benefit from more granular ranking, such as sentence-level ranking for open-domain QA, or proposition-level ranking for attribution.In this work, we introduce the idea of any-granularity ranking 1 which leverages multi-vector embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of granularity.We propose a multi-granular contrastive loss for training multi-vector approaches and validate its utility with both sentences and propositions as ranking units.Finally, we demonstrate the application of proposition-level ranking to post-hoc citation addition in retrievalaugmented generation, surpassing the performance of prompt-driven citation generation.

Large Language Models (LLMs) have significantly advanced the field of information retrieval, particularly for reranking.Listwise LLM rerankers typically showcase superior performance and generalizability over conventional supervised approaches.However, existing LLM rerankers can be inefficient as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers.Further, they are trained using the standard language modeling objective, which treats all ranking errors uniformly, potentially at the cost of misranking highly relevant passages.Addressing these limitations, we introduce FIRST 1 , a novel listwise LLM reranking approach that leverages the output logits of the first generated identifier to directly obtain a ranked ordering of the candidates.We further utilize a learning-to-rank loss for this model, which prioritizes ranking accuracy for the more relevant passages.Empirical results demonstrate that FIRST accelerates inference by 50% while maintaining robust ranking performance, with gains across the BEIR benchmark.Finally, to illustrate the practical effectiveness of listwise LLM rerankers, we investigate their application in providing relevance feedback for retrievers during inference.Our results show that LLM rerankers can provide a stronger distillation signal compared to cross-encoders, yielding substantial improvements in retriever recall after relevance feedback.

Stuti Agrawal, Pranav Pillai, Nishi Uppuluri, Revanth Gangi Reddy, Sha Li, Gokhan Tur, Dilek Hakkani-Tur, Heng Ji. Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2024.

Chatbots are often hindered by the latency associated with integrating real-time web search results, compromising user experience. To overcome this, we present CharmBana, an innovative social chatbot that introduces the use of progressive response generation to effortlessly blend search results into the bot's responses, while ensuring low response latency. The use of progressive responses is especially beneficial for voice-based chatbots, where the preliminary response buys time for a detailed follow-up, ensuring a smooth user interaction. As a result, our method not only cuts down user waiting times by 50% but also generates more relevant, precise, and engaging search inquiries. When tested in the Alexa Prize Socialbot Grand Challenge 5, our chatbot employing progressive responses consistently received higher user ratings.

A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details. In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. To this end, we create a new multi-document summarization benchmark, SumREN, comprising 745 summaries of reported statements from various public figures obtained from 633 news articles discussing 132 events. We propose an automatic silver-training data generation approach for our task, which helps smaller models like BART achieve GPT-3 level performance on this task. Finally, we introduce a pipeline-based framework for summarizing reported speech, which we empirically show to generate summaries that are more abstractive and factual than baseline query-focused summarization approaches.

Recently, there has been an increasing interest in building question answering (QA) models that reason across multiple modalities, such as text and images. However, QA using images is often limited to just picking the answer from a pre-defined set of options. In addition, images in the real world, especially in news, have objects that are co-referential to the text, with complementary information from both modalities. In this paper, we present a new QA evaluation benchmark with 1,384 questions over news articles that require cross-media grounding of objects in images onto text. Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question. In addition, we introduce a novel multimedia data augmentation framework, based on cross-media knowledge extraction and synthetic question-answer generation, to automatically augment data that can provide weak supervision for this task. We evaluate both pipeline-based and end-to-end pretraining-based multimedia QA models on our benchmark, and show that they achieve promising performance, while considerably lagging behind human performance hence leaving large room for future work on this challenging new task.

In recent years, there has been an increasing interest in claim detection as an important building block for misinformation detection. This involves detecting more fine-grained attributes relating to the claim, such as the claimer, claim topic, claim object pertaining to the topic, etc. Yet, a notable bottleneck of existing claim detection approaches is their portability to emerging events and low-resource training data settings. In this regard, we propose a fine-grained claim detection framework that leverages zero-shot Question Answering (QA) using directed questions to solve a diverse set of sub-tasks such as topic filtering, claim object detection, and claimer detection. We show that our approach significantly outperforms various zero-shot, few-shot and task-specific baselines on the NewsClaims benchmark (Reddy et al., 2021).

Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation.

Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

We show that supervised neural information retrieval (IR) models are prone to learning sparse attention patterns over passage tokens, which can result in key phrases including named entities receiving low attention weights, eventually leading to model under-performance. Using a novel targeted synthetic data generation method that identifies poorly attended entities and conditions the generation episodes on those, we teach neural IR to attend more uniformly and robustly to all entities in a given passage. On two public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.

Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, Avi Sil. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR)---a state-of-the-art (SOTA) open domain neural retrieval model---on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. To make DPR more robust under domain shift, we explore its fine-tuning with synthetic training examples, which we generate from unlabeled target domain text using a text-to-text generator. In our experiments, this noisy but fully automated target domain supervision gives DPR a sizable advantage over BM25 in out-of-domain settings, making it a more viable model in practice. Finally, an ensemble of BM25 and our improved DPR model yields the best results, further pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.

Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avi Sil, Todd Ward. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.