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Found 18 papers, 15 papers with code
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering
This motivates us to investigate whether long CoT reasoning is a general capability for LLMs.
Bayesian Analyses of Structural Vector Autoregressions with Sign, Zero, and Narrative Restrictions Using the R Package bsvarSIGNs
The R package bsvarSIGNs implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions.
DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning
They assume that problems are from the same task and traverse them in a random order.
YuLan: An Open-source Large Language Model
This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters.
Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning
The emergence of in-context learning (ICL) is potentially attributed to two major abilities: task recognition (TR) for recognizing the task from demonstrations and utilizing pre-trained priors, and task learning (TL) for learning from demonstrations.
A prediction-based forward-looking vehicle dispatching strategy for dynamic ride-pooling
For on-demand dynamic ride-pooling services, e. g., Uber Pool and Didi Pinche, a well-designed vehicle dispatching strategy is crucial for platform profitability and passenger experience.
Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers
By systematically analyzing a rich set of improvement strategies on the two aspects, we further develop a capable Gradient-inspired LLM-based Prompt Optimizer called GPO.
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
Are Large Language Models Good Prompt Optimizers?
Despite the success, the underlying mechanism of this approach remains unexplored, and the true effectiveness of LLMs as Prompt Optimizers requires further validation.
Unified Single-Stage Transformer Network for Efficient RGB-T Tracking
With this structure, the network can extract fusion features of the template and search region under the mutual interaction of modalities.
Ranked #5 on
Rgb-T Tracking
on GTOT
Alleviating the Long-Tail Problem in Conversational Recommender Systems
Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations.
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model.
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach.
Ranked #1 on
Text Generation
on ReDial
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
In recent years, conversational recommender system (CRS) has received much attention in the research community.
Spin selectivity through chiral polyalanine monolayers on semiconductors
Electrical generation of polarized spins in nonmagnetic materials is of great interest for the underlying physics and device potential.
Applied Physics Strongly Correlated Electrons
