pwc-archive/papers-with-abstracts huggingface dataset:
Learning Audio Concepts from Counterfactual Natural Language
Authors: Ali Vosoughi, Luca Bondi, Ho-Hsiang Wu, Chenliang Xu
Date: 2024-01-10 00:00:00
Tasks: Audio captioning, Audio Classification, counterfactual, Retrieval

Conventional audio classification relied on predefined classes, lacking the ability to learn from free-form text. Recent methods unlock learning joint audio-text embeddings from raw audio-text pairs describing audio in natural language. Despite recent advancements, there is little exploration of systematic methods to train models for recognizing sound events and sources in alternative scenarios, such as distinguishing fireworks from gunshots at outdoor events in similar situations. This study introduces causal reasoning and counterfactual analysis in the audio domain. We use counterfactual instances and include them in our model across different aspects. Our model considers acoustic characteristics and sound source information from human-annotated reference texts. To validate the effectiveness of our model, we conducted pre-training utilizing multiple audio captioning datasets. We then evaluate with several common downstream tasks, demonstrating the merits of the proposed method as one of the first works leveraging counterfactual information in audio domain. Specifically, the top-1 accuracy in open-ended language-based audio retrieval task increased by more than 43%.
Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations
Authors: Manqing Mao, PaiShun Ting, Yijian Xiang, Mingyang Xu, Julia Chen, Jianzhe Lin
Date: 2024-01-10 00:00:00
Tasks: Chatbot

Recent advancements in large language models (LLMs) have provided a new avenue for chatbot development. Most existing research, however, has primarily centered on single-user chatbots that determine "What" to answer. This paper highlights the complexity of multi-user chatbots, introducing the 3W design dimensions: "What" to say, "When" to respond, and "Who" to answer. Additionally, we proposed Multi-User Chat Assistant (MUCA), an LLM-based framework tailored for group discussions. MUCA consists of three main modules: Sub-topic Generator, Dialog Analyzer, and Conversational Strategies Arbitrator. These modules jointly determine suitable response contents, timings, and appropriate addressees. This paper further proposes an LLM-based Multi-User Simulator (MUS) to ease MUCA's optimization, enabling faster simulation of conversations between the chatbot and simulated users, and speeding up MUCA's early development. In goal-oriented conversations with a small to medium number of participants, MUCA demonstrates effectiveness in tasks like chiming in at appropriate timings, generating relevant content, and improving user engagement, as shown by case studies and user studies.
Attendre: Wait To Attend By Retrieval With Evicted Queries in Memory-Based Transformers for Long Context Processing
Authors: Zi Yang, Nan Hua
Date: 2024-01-10 00:00:00
Tasks: Decoder, Reading Comprehension, Retrieval, TriviaQA

As LLMs have become capable of processing more complex types of inputs, researchers have recently studied how to efficiently and affordably process possibly arbitrarily long sequences. One effective approach is to use a FIFO memory to store keys and values of an attention sublayer from past chunks to allow subsequent queries to attend. However, this approach requires a large memory and/or takes into the consideration the specific LM architecture. Moreover, due to the causal nature between the key-values in prior context and the queries at present, this approach cannot be extended to bidirectional attention such as in an encoder-decoder or PrefixLM decoder-only architecture. In this paper, we propose to use eviction policies, such as LRA and LFA, to reduce the memory size and adapt to various architectures, and we also propose the Attendre layer, a wait-to-attend mechanism by retrieving the key-value memory (K/V memory) with evicted queries in the query memory (Q memory). As a first step, we evaluate this method in the context length extension setup using the TriviaQA reading comprehension task, and show the effectiveness of the approach.
Real-time and Continuous Turn-taking Prediction Using Voice Activity Projection
Authors: Koji Inoue, Bing'er Jiang, Erik Ekstedt, Tatsuya Kawahara, Gabriel Skantze
Date: 2024-01-10 00:00:00
Tasks: CPU

A demonstration of a real-time and continuous turn-taking prediction system is presented. The system is based on a voice activity projection (VAP) model, which directly maps dialogue stereo audio to future voice activities. The VAP model includes contrastive predictive coding (CPC) and self-attention transformers, followed by a cross-attention transformer. We examine the effect of the input context audio length and demonstrate that the proposed system can operate in real-time with CPU settings, with minimal performance degradation.
Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs
Authors: Harvey Lederman, Kyle Mahowald
Date: 2024-01-10 00:00:00
Tasks: Philosophy

Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from original human-generated text. We then argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference, using new names to refer to new entities. Such examples could be explained if LLMs were not cultural technologies but had beliefs, desires, and intentions. According to interpretationism in the philosophy of mind, a system has such attitudes if and only if its behavior is well explained by the hypothesis that it does. Interpretationists may hold that LLMs have attitudes, and thus have a simple solution to the novel reference problem. We emphasize, however, that interpretationism is compatible with very simple creatures having attitudes and differs sharply from views that presuppose these attitudes require consciousness, sentience, or intelligence (topics about which we make no claims).
Entity Recognition from Colloquial Text
Authors: Tamara Babaian, Jennifer Xu
Date: 2024-01-09 00:00:00
Tasks: Management

Extraction of concepts and entities of interest from non-formal texts such as social media posts and informal communication is an important capability for decision support systems in many domains, including healthcare, customer relationship management, and others. Despite the recent advances in training large language models for a variety of natural language processing tasks, the developed models and techniques have mainly focused on formal texts and do not perform as well on colloquial data, which is characterized by a number of distinct challenges. In our research, we focus on the healthcare domain and investigate the problem of symptom recognition from colloquial texts by designing and evaluating several training strategies for BERT-based model fine-tuning. These strategies are distinguished by the choice of the base model, the training corpora, and application of term perturbations in the training data. The best-performing models trained using these strategies outperform the state-of-the-art specialized symptom recognizer by a large margin. Through a series of experiments, we have found specific patterns of model behavior associated with the training strategies we designed. We present design principles for training strategies for effective entity recognition in colloquial texts based on our findings.
Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need
Authors: Abderrahman Skiredj, Ismail Berrada
Date: 2024-01-09 00:00:00
Tasks: All, Arabic Text Diacritization, token-classification, Token Classification, Transfer Learning

Automatic diacritization of Arabic text involves adding diacritical marks (diacritics) to the text. This task poses a significant challenge with noteworthy implications for computational processing and comprehension. In this paper, we introduce PTCAD (Pre-FineTuned Token Classification for Arabic Diacritization, a novel two-phase approach for the Arabic Text Diacritization task. PTCAD comprises a pre-finetuning phase and a finetuning phase, treating Arabic Text Diacritization as a token classification task for pre-trained models. The effectiveness of PTCAD is demonstrated through evaluations on two benchmark datasets derived from the Tashkeela dataset, where it achieves state-of-the-art results, including a 20\% reduction in Word Error Rate (WER) compared to existing benchmarks and superior performance over GPT-4 in ATD tasks.
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation
Authors: Eugene Yang, Dawn Lawrie, James Mayfield, Douglas W. Oard, Scott Miller
Date: 2024-01-09 00:00:00
Tasks: Information Retrieval, Knowledge Distillation, Retrieval, Translation

Prior work on English monolingual retrieval has shown that a cross-encoder trained using a large number of relevance judgments for query-document pairs can be used as a teacher to train more efficient, but similarly effective, dual-encoder student models. Applying a similar knowledge distillation approach to training an efficient dual-encoder model for Cross-Language Information Retrieval (CLIR), where queries and documents are in different languages, is challenging due to the lack of a sufficiently large training collection when the query and document languages differ. The state of the art for CLIR thus relies on translating queries, documents, or both from the large English MS MARCO training set, an approach called Translate-Train. This paper proposes an alternative, Translate-Distill, in which knowledge distillation from either a monolingual cross-encoder or a CLIR cross-encoder is used to train a dual-encoder CLIR student model. This richer design space enables the teacher model to perform inference in an optimized setting, while training the student model directly for CLIR. Trained models and artifacts are publicly available on Huggingface.
Hierarchical Causal Models
Authors: Eli N. Weinstein, David M. Blei
Date: 2024-01-10 00:00:00
Tasks: Causal Identification

Scientists often want to learn about cause and effect from hierarchical data, collected from subunits nested inside units. Consider students in schools, cells in patients, or cities in states. In such settings, unit-level variables (e.g. each school's budget) may affect subunit-level variables (e.g. the test scores of each student in each school) and vice versa. To address causal questions with hierarchical data, we propose hierarchical causal models, which extend structural causal models and causal graphical models by adding inner plates. We develop a general graphical identification technique for hierarchical causal models that extends do-calculus. We find many situations in which hierarchical data can enable causal identification even when it would be impossible with non-hierarchical data, that is, if we had only unit-level summaries of subunit-level variables (e.g. the school's average test score, rather than each student's score). We develop estimation techniques for hierarchical causal models, using methods including hierarchical Bayesian models. We illustrate our results in simulation and via a reanalysis of the classic "eight schools" study.
Adaptive Hardness Negative Sampling for Collaborative Filtering
Authors: Riwei Lai, Rui Chen, Qilong Han, Chi Zhang, Li Chen
Date: 2024-01-10 00:00:00
Tasks: Collaborative Filtering

Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance. We experimentally unveil a common limitation of all existing negative sampling methods that they can only select negative samples of a fixed hardness level, leading to the false positive problem (FPP) and false negative problem (FNP). We then propose a new paradigm called adaptive hardness negative sampling (AHNS) and discuss its three key criteria. By adaptively selecting negative samples with appropriate hardnesses during the training process, AHNS can well mitigate the impacts of FPP and FNP. Next, we present a concrete instantiation of AHNS called AHNS_{p<0}, and theoretically demonstrate that AHNS_{p<0} can fit the three criteria of AHNS well and achieve a larger lower bound of normalized discounted cumulative gain. Besides, we note that existing negative sampling methods can be regarded as more relaxed cases of AHNS. Finally, we conduct comprehensive experiments, and the results show that AHNS_{p<0} can consistently and substantially outperform several state-of-the-art competitors on multiple datasets.
On the Influence of Reading Sequences on Knowledge Gain during Web Search
Authors: Wolfgang Gritz, Anett Hoppe, Ralph Ewerth
Date: 2024-01-10 00:00:00

Nowadays, learning increasingly involves the usage of search engines and web resources. The related interdisciplinary research field search as learning aims to understand how people learn on the web. Previous work has investigated several feature classes to predict, for instance, the expected knowledge gain during web search. Therein, eye-tracking features have not been extensively studied so far. In this paper, we extend a previously used reading model from a line-based one to one that can detect reading sequences across multiple lines. We use publicly available study data from a web-based learning task to examine the relationship between our feature set and the participants' test scores. Our findings demonstrate that learners with higher knowledge gain spent significantly more time reading, and processing more words in total. We also find evidence that faster reading at the expense of more backward regressions may be an indicator of better web-based learning. We make our code publicly available at https://github.com/TIBHannover/reading_web_search.
SARA: A Collection of Sensitivity-Aware Relevance Assessments
Authors: Jack McKechnie, Graham McDonald
Date: 2024-01-10 00:00:00
Tasks: Sensitivity, Sensitivity Classification

Large archival collections, such as email or government documents, must be manually reviewed to identify any sensitive information before the collection can be released publicly. Sensitivity classification has received a lot of attention in the literature. However, more recently, there has been increasing interest in developing sensitivity-aware search engines that can provide users with relevant search results, while ensuring that no sensitive documents are returned to the user. Sensitivity-aware search would mitigate the need for a manual sensitivity review prior to collections being made available publicly. To develop such systems, there is a need for test collections that contain relevance assessments for a set of information needs as well as ground-truth labels for a variety of sensitivity categories. The well-known Enron email collection contains a classification ground-truth that can be used to represent sensitive information, e.g., the Purely Personal and Personal but in Professional Context categories can be used to represent sensitive personal information. However, the existing Enron collection does not contain a set of information needs and relevance assessments. In this work, we present a collection of fifty information needs (topics) with crowdsourced query formulations (3 per topic) and relevance assessments (11,471 in total) for the Enron collection (mean number of relevant documents per topic = 11, variance = 34.7). The developed information needs, queries and relevance judgements are available on GitHub and will be available along with the existing Enron collection through the popular ir_datasets library. Our proposed collection results in the first freely available test collection for developing sensitivity-aware search systems.
Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis
Authors: Lanling Xu, Junjie Zhang, Bingqian Li, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao, Ji-Rong Wen
Date: 2024-01-10 00:00:00
Tasks: Prompt Engineering, Recommendation Systems

Recently, Large Language Models~(LLMs) such as ChatGPT have showcased remarkable abilities in solving general tasks, demonstrating the potential for applications in recommender systems. To assess how effectively LLMs can be used in recommendation tasks, our study primarily focuses on employing LLMs as recommender systems through prompting engineering. We propose a general framework for utilizing LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. To conduct our analysis, we formalize the input of LLMs for recommendation into natural language prompts with two key aspects, and explain how our framework can be generalized to various recommendation scenarios. As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs. As for prompt engineering, we further analyze the impact of four important components of prompts, \ie task descriptions, user interest modeling, candidate items construction and prompting strategies. In each section, we first define and categorize concepts in line with the existing literature. Then, we propose inspiring research questions followed by detailed experiments on two public datasets, in order to systematically analyze the impact of different factors on performance. Based on our empirical analysis, we finally summarize promising directions to shed lights on future research.
A Survey on Cross-Domain Sequential Recommendation
Authors: Shu Chen, Zitao Xu, Weike Pan, Qiang Yang, Zhong Ming
Date: 2024-01-10 00:00:00
Tasks: Auxiliary Learning, Sequential Recommendation, Survey

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we first define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we first discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.
Improving Tag-Clouds as Visual Information Retrieval Interfaces
Authors: Yusef Hassan-Montero, Victor Herrero-Solana
Date: 2024-01-10 00:00:00
Tasks: Information Retrieval, Retrieval, TAG

Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud's tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.
DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation
Authors: Zhiqiang Guo, GuoHui Li, Jianjun Li, Chaoyang Wang, Si Shi
Date: 2024-01-10 00:00:00
Tasks: Collaborative Filtering, Disentanglement, Representation Learning, Variational Inference

Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.
Answer Retrieval in Legal Community Question Answering
Authors: Arian Askari, Zihui Yang, Zhaochun Ren, Suzan Verberne
Date: 2024-01-09 00:00:00
Tasks: Community Question Answering, Question Answering, Retrieval

The task of answer retrieval in the legal domain aims to help users to seek relevant legal advice from massive amounts of professional responses. Two main challenges hinder applying existing answer retrieval approaches in other domains to the legal domain: (1) a huge knowledge gap between lawyers and non-professionals; and (2) a mix of informal and formal content on legal QA websites. To tackle these challenges, we propose CE_FS, a novel cross-encoder (CE) re-ranker based on the fine-grained structured inputs. CE_FS uses additional structured information in the CQA data to improve the effectiveness of cross-encoder re-rankers. Furthermore, we propose LegalQA: a real-world benchmark dataset for evaluating answer retrieval in the legal domain. Experiments conducted on LegalQA show that our proposed method significantly outperforms strong cross-encoder re-rankers fine-tuned on MS MARCO. Our novel finding is that adding the question tags of each question besides the question description and title into the input of cross-encoder re-rankers structurally boosts the rankers' effectiveness. While we study our proposed method in the legal domain, we believe that our method can be applied in similar applications in other domains.