MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning

Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge. Several studies have leveraged text retrieval to augment the models' commonsense ability. Unlike text, images capture commonsense information inherently but little effort has been paid to effectively utilize them. In this work, we propose a novel Multi-mOdal REtrieval (MORE) augmentation framework, to leverage both text and images to enhance the commonsense ability of language models. Extensive experiments on the Common-Gen task have demonstrated the efficacy of MORE based on the pre-trained models of both single and multiple modalities.

When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation

Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. However, due to the extra overhead and unassured quality of retrieval, it may not be optimal to conduct RA all the time. A straightforward idea is to only conduct retrieval when LLMs are uncertain about a question. This motivates us to enhance the LLMs' ability to perceive their knowledge boundaries to help RA. In this paper, we first quantitatively measure LLMs' such ability and confirm their overconfidence. Then, we study how LLMs' certainty about a question correlates with their dependence on external retrieved information. We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence. Additionally, equipped with these methods, LLMs can achieve comparable or even better performance of RA with much fewer retrieval calls.

Reproducibility Analysis and Enhancements for Multi-Aspect Dense Retriever with Aspect Learning

Multi-aspect dense retrieval aims to incorporate aspect information (e.g., brand and category) into dual encoders to facilitate relevance matching. As an early and representative multi-aspect dense retriever, MADRAL learns several extra aspect embeddings and fuses the explicit aspects with an implicit aspect "OTHER" for final representation. MADRAL was evaluated on proprietary data and its code was not released, making it challenging to validate its effectiveness on other datasets. We failed to reproduce its effectiveness on the public MA-Amazon data, motivating us to probe the reasons and re-examine its components. We propose several component alternatives for comparisons, including replacing "OTHER" with "CLS" and representing aspects with the first several content tokens. Through extensive experiments, we confirm that learning "OTHER" from scratch in aspect fusion is harmful. In contrast, our proposed variants can greatly enhance the retrieval performance. Our research not only sheds light on the limitations of MADRAL but also provides valuable insights for future studies on more powerful multi-aspect dense retrieval models. Code will be released at: https://github.com/sunxiaojie99/Reproducibility-for-MADRAL.

A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval

Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as category and brand. Recent work has proposed two approaches to incorporate the aspect information into item representations for effective retrieval by predicting the values associated with the item aspects. Despite their efficacy, they treat the values as isolated classes (e.g., "Smart Homes", "Home, Garden & Tools", and "Beauty & Health") and ignore their fine-grained semantic relation. Furthermore, they either enforce the learning of aspects into the CLS token, which could confuse it from its designated use for representing the entire content semantics, or learn extra aspect embeddings only with the value prediction objective, which could be insufficient especially when there are no annotated values for an item aspect. Aware of these limitations, we propose a MUlti-granulaRity-aware Aspect Learning model (MURAL) for multi-aspect dense retrieval. It leverages aspect information across various granularities to capture both coarse and fine-grained semantic relations between values. Moreover, MURAL incorporates separate aspect embeddings as input to transformer encoders so that the masked language model objective can assist implicit aspect learning even without aspect-value annotations. Extensive experiments on two real-world datasets of products and mini-programs show that MURAL outperforms state-of-the-art baselines significantly.

CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval

The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently. Since various matching patterns can exist between queries and relevant documents, previous work tries to combine multiple retrieval models to find as many relevant results as possible. The constructed ensembles, whether learned independently or jointly, do not care which component model is more suitable to an instance during training. Thus, they cannot fully exploit the capabilities of different types of retrieval models in identifying diverse relevance patterns. Motivated by this observation, in this paper, we propose a Mixture-of-Experts (MoE) model consisting of representative matching experts and a novel competitive learning mechanism to let the experts develop and enhance their expertise during training. Specifically, our MoE model shares the bottom layers to learn common semantic representations and uses differently structured upper layers to represent various types of retrieval experts. Our competitive learning mechanism has two stages: (1) a standardized learning stage to train the experts equally to develop their capabilities to conduct relevance matching; (2) a specialized learning stage where the experts compete with each other on every training instance and get rewards and updates according to their performance to enhance their expertise on certain types of samples. Experimental results on three retrieval benchmark datasets show that our method significantly outperforms the state-of-the-art baselines.

CIR at the NTCIR-17 ULTRE-2 Task

The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the ULTRE-2 task. We recognize the issue of false negatives in the Baidu search data in this competition is very severe, much more severe than position bias. Hence, we adopt the Dual Learning Algorithm (DLA) to address the position bias and use it as an auxiliary model to study how to alleviate the false negative issue. We approach the problem from two perspectives: 1) correcting the labels for non-clicked items by a relevance judgment model trained from DLA, and learn a new ranker that is initialized from DLA; 2) including random documents as true negatives and documents that have partial matching as hard negatives. Both methods can enhance the model performance and our best method has achieved nDCG@10 of 0.5355, which is 2.66% better than the best score from the organizer.

A Comparative Study of Training Objectives for Clarification Facet Generation

Due to the ambiguity and vagueness of a user query, it is essential to identify the query facets for the clarification of user intents. Existing work on query facet generation has achieved compelling performance by sequentially predicting the next facet given previously generated facets based on pre-trained language generation models such as BART. Given a query, there are mainly two types of training objectives to guide the facet generation models. One is to generate the default sequence of ground-truth facets, and the other is to enumerate all the permutations of ground-truth facets and use the sequence that has the minimum loss for model updates. The second is permutation-invariant while the first is not. In this paper, we aim to conduct a systematic comparative study of various types of training objectives, with different properties of not only whether it is permutation-invariant but also whether it conducts sequential prediction and whether it can control the count of output facets. To this end, we propose another three training objectives of different aforementioned properties. For comprehensive comparisons, besides the commonly used evaluation that measures the matching with ground-truth facets, we also introduce two diversity metrics to measure the diversity of the generated facets. Based on an open-domain query facet dataset, i.e., MIMICS, we conduct extensive analyses and show the pros and cons of each method, which could shed light on model training for clarification facet generation. The code can be found at \url{https://github.com/ShiyuNee/Facet-Generation}

L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations

First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection. While existing retrieval models have achieved impressive performance, they are mostly studied on static data sets, ignoring that in the real-world, the data on the Web is continuously growing with potential distribution drift. Consequently, retrievers trained on static old data may not suit new-coming data well and inevitably produce sub-optimal results. In this work, we study lifelong learning for first-stage retrieval, especially focusing on the setting where the emerging documents are unlabeled since relevance annotation is expensive and may not keep up with data emergence. Under this setting, we aim to develop model updating with two goals: (1) to effectively adapt to the evolving distribution with the unlabeled new-coming data, and (2) to avoid re-inferring all embeddings of old documents to efficiently update the index each time the model is updated. We first formalize the task and then propose a novel Lifelong Learning method for the first-stage Retrieval, namely L^2R. L^2R adopts the typical memory mechanism for lifelong learning, and incorporates two crucial components: (1) selecting diverse support negatives for model training and memory updating for effective model adaptation, and (2) a ranking alignment objective to ensure the backward-compatibility of representations to save the cost of index rebuilding without hurting the model performance. For evaluation, we construct two new benchmarks from LoTTE and Multi-CPR datasets to simulate the document distribution drift in realistic retrieval scenarios. Extensive experiments show that L^2R significantly outperforms competitive lifelong learning baselines.

Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval

Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.

Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search

An effective ranking model usually requires a large amount of training data to learn the relevance between documents and queries. User clicks are often used as training data since they can indicate relevance and are cheap to collect, but they contain substantial bias and noise. There has been some work on mitigating various types of bias in simulated user clicks to train effective learning-to-rank models based on multiple features. However, how to effectively use such methods on large-scale pre-trained models with real-world click data is unknown. To alleviate the data bias in the real world, we incorporate heuristic-based features, refine the ranking objective, add random negatives, and calibrate the propensity calculation in the pre-training stage. Then we fine-tune several pre-trained models and train an ensemble model to aggregate all the predictions from various pre-trained models with human-annotation data in the fine-tuning stage. Our approaches won 3rd place in the "Pre-training for Web Search" task in WSDM Cup 2023 and are 22.6% better than the 4th-ranked team.