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Found 25 papers, 16 papers with code
Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models
Assessing how well a large language model (LLM) understands human, rather than merely text, remains an open challenge.
The Lighthouse of Language: Enhancing LLM Agents via Critique-Guided Improvement
In this work, we introduce Critique-Guided Improvement (CGI), a novel two-player framework, comprising an actor model that explores an environment and a critic model that generates detailed nature language feedback.
ParallelComp: Parallel Long-Context Compressor for Length Extrapolation
To mitigate the attention sink issue, we propose an attention calibration strategy that reduces biases, ensuring more stable long-range attention.
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning
This paper introduces a novel security threat to FedPEFT, termed PEFT-as-an-Attack (PaaA), which exposes how PEFT can be exploited as an attack vector to circumvent PLMs' safety alignment and generate harmful content in response to malicious prompts.
UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference
By grouping layers and heads based on their uncertainty, UNComp adaptively compresses both the hidden states and the KV cache.
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation
We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks.
Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism
Recent research presents two conflicting views on ICL: One emphasizes the impact of similar examples in the demonstrations, stressing the need for label correctness and more shots.
Synergizing Foundation Models and Federated Learning: A Survey
The recent development of Foundation Models (FMs), represented by large language models, vision transformers, and multimodal models, has been making a significant impact on both academia and industry.
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality
We present a demonstration of a multimodal fine-grained training assistant for LEGO brick assembly in a pilot XR environment.
Anchor-based Large Language Models
Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation.
Benchmarking LLMs via Uncertainty Quantification
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models (LLMs): domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search.
Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking
Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text.
Turn-Level Active Learning for Dialogue State Tracking
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems.
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting
Furthermore, we propose distilling the rewriting capabilities of LLMs into smaller models to reduce rewriting latency.
Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process
It assumes that the performance of a dialogue system can be measured by user satisfaction and uses an estimator to simulate users.
MetaASSIST: Robust Dialogue State Tracking with Meta Learning
Experimental results demonstrate that all three schemes can achieve competitive performance.
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.
ASSIST: Towards Label Noise-Robust Dialogue State Tracking
In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels.
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation
The annotations in the training set remain unchanged (same as MultiWOZ 2. 1) to elicit robust and noise-resilient model training.
Slot Self-Attentive Dialogue State Tracking
Then a stacked slot self-attention is applied on these features to learn the correlations among slots.
Auto-weighted Robust Federated Learning with Corrupted Data Sources
In this paper, we address this challenge by proposing Auto-weighted Robust Federated Learning (arfl), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources.
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling
Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes.
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates.
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches.
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