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Found 76 papers, 30 papers with code
A Transformer-based Threshold-Free Framework for Multi-Intent NLU
Furthermore, we propose a threshold-free intent multi-intent classifier that utilizes the output of IND task and detects the multiple intents without depending on the threshold.
Ranked #3 on
Semantic Frame Parsing
on MixSNIPS
MultiFinBen: A Multilingual, Multimodal, and Difficulty-Aware Benchmark for Financial LLM Evaluation
We introduce MultiFinBen, the first multilingual and multimodal benchmark tailored to the global financial domain, evaluating LLMs across modalities (text, vision, audio) and linguistic settings (monolingual, bilingual, multilingual) on domain-specific tasks.
MiMo-VL Technical Report
We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models delivering state-of-the-art performance in both general visual understanding and multimodal reasoning.
LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning
We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection.
XtraGPT: LLMs for Human-AI Collaboration on Controllable Academic Paper Revision
Despite the growing adoption of large language models (LLMs) in academic workflows, their capabilities remain limited when it comes to supporting high-quality scientific writing.
MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining
We present MiMo-7B, a large language model born for reasoning tasks, with optimization across both pre-training and post-training stages.
StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation
However, in real-world deployments, we observe that the colocated architecture suffers from resource coupling, where the two stages are constrained to use the same resources.
Assessing Judging Bias in Large Reasoning Models: An Empirical Study
Large Reasoning Models (LRMs) like DeepSeek-R1 and OpenAI-o1 have demonstrated remarkable reasoning capabilities, raising important questions about their biases in LLM-as-a-judge settings.
JudgeLRM: Large Reasoning Models as a Judge
The rise of Large Language Models (LLMs) as evaluators offers a scalable alternative to human annotation, yet existing Supervised Fine-Tuning (SFT) for judges approaches often fall short in domains requiring complex reasoning.
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era
Image captioning has been a longstanding challenge in vision-language research.
SolBench: A Dataset and Benchmark for Evaluating Functional Correctness in Solidity Code Completion and Repair
To fill this gap, we introduce SolBench, a benchmark for evaluating the functional correctness of Solidity smart contracts generated by code completion models.
Task-Oriented 6-DoF Grasp Pose Detection in Clutters
With an aim to get more general and practical grasp models, in this paper, we investigate the problem named Task-Oriented 6-DoF Grasp Pose Detection in Clutters (TO6DGC), which extends the task-oriented problem to a more general 6-DOF Grasp Pose Detection in Cluttered (multi-object) scenario.
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query.
Evaluating Conversational Recommender Systems with Large Language Models: A User-Centric Evaluation Framework
Conversational recommender systems (CRS) involve both recommendation and dialogue tasks, which makes their evaluation a unique challenge.
What Limits LLM-based Human Simulation: LLMs or Our Design?
We argue that advancing LLM-based human simulation requires addressing both LLM's inherent limitations and simulation framework design challenges.
Explainable Saliency: Articulating Reasoning with Contextual Prioritization
Deep saliency models, which predict what parts of an image capture our attention, are often like black boxes.
Data Driven Automatic Electrical Machine Preliminary Design with Artificial Intelligence Expert Guidance
Once trained, guided by metaheuristic algorithms, the surrogate model can generate thousands of geometric scalable designs, covering a wide power range, forming an AI expert database to guide future preliminary design.
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
Furthermore, GCoder efficiently manages large-scale graphs with millions of nodes and diverse input formats, overcoming the limitations of previous models focused on the reasoning steps paradigm.
Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts
In order to leverage the generalization capability of multilingual LLMs to efficiently scale to more resource-constrained languages, we explore the internal information flow of LLMs from a multilingual perspective using Mixture of Experts (MoE) modularity.
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation
To address these issues, we propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency.
AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment
Our finding demonstrates that LLM%u2019s judgments, similar to human judgments, are also influenced by threshold priming biases, and suggests that researchers and system engineers should take into account potential human-like cognitive biases in designing, evaluating, and auditing LLMs in IR tasks and beyond.
ControlMath: Controllable Data Generation Promotes Math Generalist Models
The module creates diverse equations, which the Problem-Crafter agent then transforms into math word problems.
Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis
We explore adapting foundation models (FMs) from the computer vision domain to geoscience.
The Oscars of AI Theater: A Survey on Role-Playing with Language Models
This survey explores the burgeoning field of role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simulations facilitated by Large Language Models (LLMs).
A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading
The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions.
$α$-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction
To address this, we propose an uncertainty-aware OCC method ($\alpha$-OCC).
3D Semantic Occupancy Prediction
3D Semantic Scene Completion
+4
Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset
More specifically, MARS is collected with a fleet of autonomous vehicles driving within a certain geographical area.
Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when facing malicious adversarial attacks.
Is Your LLM Outdated? Evaluating LLMs at Temporal Generalization
The rapid advancement of Large Language Models (LLMs) highlights the urgent need for evolving evaluation methodologies that keep pace with improvements in language comprehension and information processing.
Vector Quantization for Recommender Systems: A Review and Outlook
Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems.
Structure-aware Fine-tuning for Code Pre-trained Models
Over the past few years, we have witnessed remarkable advancements in Code Pre-trained Models (CodePTMs).
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect.
Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources.
GraphWiz: An Instruction-Following Language Model for Graph Problems
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored.
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
Existing retrieval-based methods have made significant strides in maintaining long-term conversations.
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting
IMP-TIP follows the ``From Good to Great" concept, collecting multiple potential solutions from both LLMs and their Tool-Augmented counterparts for the same math problem, and then selecting or re-generating the most accurate answer after cross-checking these solutions via tool-augmented interleaf prompting.
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing.
Light Field Imaging in the Restrictive Object Space based on Flexible Angular Plane
The light field imaging in restrictive object space (ROS-LF) is complicated but significant.
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
To this end, in our paper, we propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with per-sample criteria using potent MLLM as the judge.
Decoy Effect in Search Interaction: A Pilot Study
In recent years, the influence of cognitive effects and biases on users' thinking, behaving, and decision-making has garnered increasing attention in the field of interactive information retrieval.
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks.
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding
The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios.
ChatDev: Communicative Agents for Software Development
Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing.
A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power
In this study, we investigate the statistical stability of C/W/L/A metrics from the perspective of: (1) the system ranking similarity among aggregations, (2) the system ranking consistency of aggregations and (3) the discriminative power of aggregations.
SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving
Monocular scene understanding is a foundational component of autonomous systems.
3D Semantic Scene Completion
3D Semantic Scene Completion from a single 2D image
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning.
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
In this paper, we introduce gradient descent into black-box tuning scenario through knowledge distillation.
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives
Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks.
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services.
Alleviating Over-smoothing for Unsupervised Sentence Representation
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities.
Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision
Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels.
Improve Retrieval-based Dialogue System via Syntax-Informed Attention
Since incorporating syntactic information like dependency structures into neural models can promote a better understanding of the sentences, such a method has been widely used in NLP tasks.
HugNLP: A Unified and Comprehensive Library for Natural Language Processing
In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios.
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities.
Natural Response Generation for Chinese Reading Comprehension
To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios.
Chinese Reading Comprehension
Machine Reading Comprehension
+1
Bridge the Gap between Language models and Tabular Understanding
For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment.
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks
The noise is due to a fatal bug in the backend of our relevance assessment interface.
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
To interpret these models, some probing methods have been applied.
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i. e., passage and question.
Automatic Prosody Annotation with Pre-Trained Text-Speech Model
Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability.
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.
Ranked #1 on
Spoken Language Understanding
on Spoken-SQuAD
Detecting Recolored Image by Spatial Correlation
In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring.
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages.
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension
Cross-lingual Machine Reading Comprehension (xMRC) is challenging due to the lack of training data in low-resource languages.
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering
In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage.
Towards Visual Explainable Active Learning for Zero-Shot Classification
This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems.
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting
Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size.
Self-supervised Dialogue Learning for Spoken Conversational Question Answering
We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence.
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.
Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array
In this paper, we present a new scenario of direction of arrival (DOA) estimation using massive multiple-input multiple-output (MIMO) receive array with low-resolution analog-to-digital convertors (ADCs), which can strike a good balance between performance and circuit cost.
Information Theory Signal Processing Information Theory
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering
Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora.
Audio Signal Processing
Conversational Question Answering
+2
Knowledge Distillation for Improved Accuracy in Spoken Question Answering
However, the recent work shows that ASR systems generate highly noisy transcripts, which critically limit the capability of machine comprehension on the SQA task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Towards Data Distillation for End-to-end Spoken Conversational Question Answering
In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
